Data Wrangling and Analyzing - Project 4

Udacity Data Analyst Nanodegree

by Sara Vicente

Table of Contents

• Introduction
• Gathering Data
• Assessing Data
• Cleaning Data
• Data Analysis & Conclusions
• Sources

INTRODUCTION

By using Python and its libraries, in this project, I will load in the data, check for cleanliness, and then trim and clean your dataset for analysis:

• Gather data from three sources.
• Assess its quality and tidiness.
• Clean the data.

After wrangling the data I will analyze the dataset and get some conclusions about it.

GATHERING DATA

The datasets I will use are the ones described below:

1. First, I will gather data from a Twitter Archive of the famous Twitter account @dog_rates (WeRateDogs), in which users leave comments rating their dogs adding a funny comment to the rating. It is noteworthy that usually, the ratings have a denominator of 10 with numerators greater than this number, for example: 12/10.

2. Then, we will collect the number of retweets and the number of favorites per tweet, since this information is missing in our first dataset. I will obtain this information using Tweepy to query Twitter's API.

3. The last dataset is contributed by Udacity, since the course instructor collected images from the WeRateDogs archive and classified them by breed through neural networks. Thus, I obtain a table with the predictions of each dog image, the tweet ID, the image URL and the image number that corresponds to the safest prediction.

# Import all the libraries that I will be using for this project
import pandas as pd
import numpy as np
import requests
import os
import re
import seaborn as sns
import matplotlib.pyplot as plt 
%matplotlib inline

Twitter Archive

# Read the twitter-archive-enhanced.csv and import it into a DataFrame
pd.set_option('display.max_colwidth', -1) # This will set the no truncate for pandas as well as for dask.
df_twitter = pd.read_csv('twitter-archive-enhanced.csv')
df_twitter.head()

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643555336193/photo/1	13	10	Phineas	None	None	None	None
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421306343426/photo/1	13	10	Tilly	None	None	None	None
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181378084864/photo/1	12	10	Archie	None	None	None	None
3	891689557279858688	NaN	NaN	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891689557279858688/photo/1	13	10	Darla	None	None	None	None
4	891327558926688256	NaN	NaN	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1	12	10	Franklin	None	None	None	None

Twitter's API

I had some issues due to the current Covid-19 situation and it was not possible to get the approval for the Twitter's developper account as soon as I would have liked for this project, as it is taking them longer. Therefore I had tu use the data already provided by Udacity: (tweet_json.txt).

import tweepy
from tweepy import OAuthHandler
import json
from timeit import default_timer as timer

# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file
# These are hidden to comply with Twitter's API terms and conditions
consumer_key = 'HIDEN'
consumer_secret = 'HIDEN'
access_token = 'HIDEN'
access_secret = 'HIDEN'

auth = OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_secret)

api = tweepy.API(auth, wait_on_rate_limit=True)

# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:
# df_1 is a DataFrame with the twitter_archive_enhanced.csv file. You may have to
# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv
# NOTE TO REVIEWER: this student had mobile verification issues so the following
# Twitter API code was sent to this student from a Udacity instructor
# Tweet IDs for which to gather additional data via Twitter's API
tweet_ids = df_twitter.tweet_id.values
len(tweet_ids)

# Query Twitter's API for JSON data for each tweet ID in the Twitter archive
count = 0
fails_dict = {}
start = timer()
# Save each tweet's returned JSON as a new line in a .txt file
with open('tweet_json.txt', 'w') as outfile:
    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit
    for tweet_id in tweet_ids:
        count += 1
        print(str(count) + ": " + str(tweet_id))
        try:
            tweet = api.get_status(tweet_id, tweet_mode='extended')
            print("Success")
            json.dump(tweet._json, outfile)
            outfile.write('\n')
        except tweepy.TweepError as e:
            print("Fail")
            fails_dict[tweet_id] = e
            pass
end = timer()
print(end - start)
print(fails_dict)

1: 892420643555336193
Fail
2: 892177421306343426
Fail
3: 891815181378084864
Fail
4: 891689557279858688
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5: 891327558926688256
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6: 891087950875897856
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
1973: 673240798075449344
Fail
1974: 673213039743795200
Fail
1975: 673148804208660480
Fail
1976: 672997845381865473
Fail
1977: 672995267319328768
Fail
1978: 672988786805112832
Fail
1979: 672984142909456390
Fail
1980: 672980819271634944
Fail
1981: 672975131468300288
Fail
1982: 672970152493887488
Fail
1983: 672968025906282496
Fail
1984: 672964561327235073
Fail
1985: 672902681409806336
Fail
1986: 672898206762672129
Fail
1987: 672884426393653248
Fail
1988: 672877615439593473
Fail
1989: 672834301050937345
Fail
1990: 672828477930868736
Fail
1991: 672640509974827008
Fail
1992: 672622327801233409
Fail
1993: 672614745925664768
Fail
1994: 672609152938721280
Fail
1995: 672604026190569472
Fail
1996: 672594978741354496
Fail
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Fail
1998: 672591271085670400
Fail
1999: 672538107540070400
Fail
2000: 672523490734551040
Fail
2001: 672488522314567680
Fail
2002: 672482722825261057
Fail
2003: 672481316919734272
Fail
2004: 672475084225949696
Fail
2005: 672466075045466113
Fail
2006: 672272411274932228
Fail
2007: 672267570918129665
Fail
2008: 672264251789176834
Fail
2009: 672256522047614977
Fail
2010: 672254177670729728
Fail
2011: 672248013293752320
Fail
2012: 672245253877968896
Fail
2013: 672239279297454080
Fail
2014: 672231046314901505
Fail
2015: 672222792075620352
Fail
2016: 672205392827572224
Fail
2017: 672169685991993344
Fail
2018: 672160042234327040
Fail
2019: 672139350159835138
Fail
2020: 672125275208069120
Fail
2021: 672095186491711488
Fail
2022: 672082170312290304
Fail
2023: 672068090318987265
Fail
2024: 671896809300709376
Fail
2025: 671891728106971137
Fail
2026: 671882082306625538
Fail
2027: 671879137494245376
Fail
2028: 671874878652489728
Fail
2029: 671866342182637568
Fail
2030: 671855973984772097
Fail
2031: 671789708968640512
Fail
2032: 671768281401958400
Fail
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Fail
2034: 671744970634719232
Fail
2035: 671743150407421952
Fail
2036: 671735591348891648
Fail
2037: 671729906628341761
Fail
2038: 671561002136281088
Fail
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Fail
2040: 671547767500775424
Fail
2041: 671544874165002241
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2050: 671504605491109889
Fail
2051: 671497587707535361
Fail
2052: 671488513339211776
Fail
2053: 671486386088865792
Fail
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Fail
2055: 671390180817915904
Fail
2056: 671362598324076544
Fail
2057: 671357843010908160
Fail
2058: 671355857343524864
Fail
2059: 671347597085433856
Fail
2060: 671186162933985280
Fail
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Fail
2062: 671166507850801152
Fail
2063: 671163268581498880
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2071: 671122204919246848
Fail
2072: 671115716440031232
Fail
2073: 671109016219725825
Fail
2074: 670995969505435648
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2081: 670823764196741120
Fail
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Fail
2083: 670815497391357952
Fail
2084: 670811965569282048
Fail
2085: 670807719151067136
Fail
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Fail
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Fail
2088: 670797304698376195
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2354: 666033412701032449
Fail
2355: 666029285002620928
Fail
2356: 666020888022790149
Fail
373.00385841
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TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831670449226514432: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831650051525054464: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831552930092285952: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831322785565769729: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831315979191906304: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831309418084069378: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831262627380748289: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830956169170665475: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830583320585068544: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830173239259324417: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830097400375152640: TweepError([{'code': 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TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690597161306841088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690400367696297985: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690374419777196032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690360449368465409: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690348396616552449: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690248561355657216: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690021994562220032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690015576308211712: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690005060500217858: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 689999384604450816: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 689993469801164801: TweepError([{'code': 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668988183816871936: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668986018524233728: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668981893510119424: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668979806671884288: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668975677807423489: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668967877119254528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668960084974809088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668955713004314625: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668932921458302977: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668902994700836864: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668892474547511297: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668872652652679168: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668852170888998912: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668826086256599040: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668815180734689280: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668779399630725120: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668655139528511488: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668645506898350081: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668643542311546881: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668641109086707712: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668636665813057536: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668633411083464705: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668631377374486528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668627278264475648: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668625577880875008: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668623201287675904: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668620235289837568: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668614819948453888: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668587383441514497: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668567822092664832: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668544745690562560: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668542336805281792: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668537837512433665: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668528771708952576: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668507509523615744: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668496999348633600: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668484198282485761: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668480044826800133: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668466899341221888: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668297328638447616: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668291999406125056: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668286279830867968: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668274247790391296: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668268907921326080: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668256321989451776: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668248472370458624: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668237644992782336: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668226093875376128: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668221241640230912: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668204964695683073: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668190681446379520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668171859951755264: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668154635664932864: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668142349051129856: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668113020489474048: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667937095915278337: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667924896115245057: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667915453470232577: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667911425562669056: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667902449697558528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667886921285246976: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667885044254572545: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667878741721415682: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667873844930215936: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667866724293877760: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667861340749471744: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667832474953625600: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667806454573760512: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667801013445750784: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667793409583771648: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667782464991965184: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667773195014021121: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667766675769573376: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667728196545200128: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667724302356258817: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667550904950915073: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667550882905632768: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667549055577362432: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667546741521195010: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667544320556335104: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667538891197542400: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667534815156183040: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667530908589760512: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667524857454854144: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667517642048163840: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667509364010450944: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667502640335572993: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667495797102141441: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667491009379606528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667470559035432960: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667455448082227200: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667453023279554560: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667443425659232256: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667437278097252352: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667435689202614272: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667405339315146752: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667393430834667520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667369227918143488: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667211855547486208: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667200525029539841: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667192066997374976: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667188689915760640: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667182792070062081: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667177989038297088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667176164155375616: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667174963120574464: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667171260800061440: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667165590075940865: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667160273090932737: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667152164079423490: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667138269671505920: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667119796878725120: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667090893657276420: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667073648344346624: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667070482143944705: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667065535570550784: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667062181243039745: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667044094246576128: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667012601033924608: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666996132027977728: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666983947667116034: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666837028449972224: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666835007768551424: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666826780179869698: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666817836334096384: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666804364988780544: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666786068205871104: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666781792255496192: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666776908487630848: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666739327293083650: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666701168228331520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666691418707132416: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666649482315059201: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666644823164719104: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666454714377183233: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666447344410484738: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666437273139982337: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666435652385423360: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666430724426358785: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666428276349472768: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666421158376562688: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666418789513326592: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666411507551481857: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666407126856765440: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666396247373291520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666373753744588802: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666362758909284353: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666353288456101888: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666345417576210432: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666337882303524864: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666293911632134144: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666287406224695296: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666273097616637952: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666268910803644416: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666104133288665088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666102155909144576: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666099513787052032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666094000022159362: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666082916733198337: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666073100786774016: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666071193221509120: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666063827256086533: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666058600524156928: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666057090499244032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666055525042405380: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666051853826850816: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666050758794694657: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666049248165822465: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666044226329800704: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666033412701032449: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666029285002620928: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666020888022790149: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],)}

# Read the JSON archive and create a DataFrame with the information needed
df_tw_json = open('tweet-json.txt', 'r')
df_twitter_counts = pd.DataFrame(columns=['tweet_id', 'retweets', 'favorites'])

for line in df_tw_json:
    tweet = json.loads(line)
    df_twitter_counts = df_twitter_counts.append({'tweet_id': tweet['id'],
                                                  'retweets': tweet['retweet_count'],
                                                  'favorites': tweet['favorite_count']},
                                                 ignore_index=True)
df_tw_json.close()

df_twitter_counts

	tweet_id	retweets	favorites
0	892420643555336193	8853	39467
1	892177421306343426	6514	33819
2	891815181378084864	4328	25461
3	891689557279858688	8964	42908
4	891327558926688256	9774	41048
5	891087950875897856	3261	20562
6	890971913173991426	2158	12041
7	890729181411237888	16716	56848
8	890609185150312448	4429	28226
9	890240255349198849	7711	32467
10	890006608113172480	7624	31166
11	889880896479866881	5156	28268
12	889665388333682689	8538	38818
13	889638837579907072	4735	27672
14	889531135344209921	2321	15359
15	889278841981685760	5637	25652
16	888917238123831296	4709	29611
17	888804989199671297	4559	26080
18	888554962724278272	3732	20290
19	888078434458587136	3653	22201
20	887705289381826560	5609	30779
21	887517139158093824	12082	46959
22	887473957103951883	18781	69871
23	887343217045368832	10737	34222
24	887101392804085760	6167	31061
25	886983233522544640	8084	35859
26	886736880519319552	3443	12306
27	886680336477933568	4610	22798
28	886366144734445568	3316	21524
29	886267009285017600	4	117
...	...	...	...
2324	666411507551481857	339	459
2325	666407126856765440	44	113
2326	666396247373291520	92	172
2327	666373753744588802	100	194
2328	666362758909284353	595	804
2329	666353288456101888	77	229
2330	666345417576210432	146	307
2331	666337882303524864	96	204
2332	666293911632134144	368	522
2333	666287406224695296	71	152
2334	666273097616637952	82	184
2335	666268910803644416	37	108
2336	666104133288665088	6871	14765
2337	666102155909144576	16	81
2338	666099513787052032	73	164
2339	666094000022159362	79	169
2340	666082916733198337	47	121
2341	666073100786774016	174	335
2342	666071193221509120	67	154
2343	666063827256086533	232	496
2344	666058600524156928	61	115
2345	666057090499244032	146	304
2346	666055525042405380	261	448
2347	666051853826850816	879	1253
2348	666050758794694657	60	136
2349	666049248165822465	41	111
2350	666044226329800704	147	311
2351	666033412701032449	47	128
2352	666029285002620928	48	132
2353	666020888022790149	532	2535

2354 rows × 3 columns

Image Predictions

# Download programatically the image prediction file using the link provided by Udacity
url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'
r = requests.get(url, allow_redirects=True)

with open('image-predictions.tsv', mode ='wb') as file:
    file.write(r.content)
    
# Read and display the data from the image predictions file
df_img_pred = pd.read_csv('image-predictions.tsv', sep = '\t')
df_img_pred.head()

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True

ASSESSING DATA

First of all I will display the original datasets' first rows and get their information to have an idea of what I will be wrangling.

df_twitter.head()

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643555336193/photo/1	13	10	Phineas	None	None	None	None
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421306343426/photo/1	13	10	Tilly	None	None	None	None
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181378084864/photo/1	12	10	Archie	None	None	None	None
3	891689557279858688	NaN	NaN	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891689557279858688/photo/1	13	10	Darla	None	None	None	None
4	891327558926688256	NaN	NaN	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1	12	10	Franklin	None	None	None	None

df_twitter_counts.head()

	tweet_id	retweets	favorites
0	892420643555336193	8853	39467
1	892177421306343426	6514	33819
2	891815181378084864	4328	25461
3	891689557279858688	8964	42908
4	891327558926688256	9774	41048

df_img_pred.head()

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True

# Get information about the columns entries and theur datatype
df_twitter.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
tweet_id                      2356 non-null int64
in_reply_to_status_id         78 non-null float64
in_reply_to_user_id           78 non-null float64
timestamp                     2356 non-null object
source                        2356 non-null object
text                          2356 non-null object
retweeted_status_id           181 non-null float64
retweeted_status_user_id      181 non-null float64
retweeted_status_timestamp    181 non-null object
expanded_urls                 2297 non-null object
rating_numerator              2356 non-null int64
rating_denominator            2356 non-null int64
name                          2356 non-null object
doggo                         2356 non-null object
floofer                       2356 non-null object
pupper                        2356 non-null object
puppo                         2356 non-null object
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB

The WeRateDogs Twitter archive contains basic tweet data for all 5000+ of their tweets, but not everything. One column the archive does contain though: each tweet's text, which I used to extract rating, dog name, and dog "stage" (i.e. doggo, floofer, pupper, and puppo) to make this Twitter archive "enhanced." Of the 5000+ tweets, I have filtered for tweets with ratings only (there are 2356).

df_twitter (Twitter archive) columns:

• tweet_id: unique identifier for each tweet
• in_reply_to_status_id: if the represented Tweet is a reply, this field will contain the integer representation of the original Tweet’s ID
• in_reply_to_user_id: if the represented Tweet is a reply, this field will contain the integer representation of the original Tweet’s author ID
• timestamp: time when the Tweet was created
• source: utility used to post the Tweet
• text: actual UTF-8 text of the status update
• retweeted_status_id: if the represented Tweet is a retweet, this field will contain the integer representation of the original Tweet’s ID
• retweeted_status_user_id: if the represented Tweet is a retweet, this field will contain the integer representation of the original Tweet’s author ID
• retweeted_status_timestamp: time of retweet
• expanded_urls: tweet URL
• rating_numerator: numerator of the rating of a dog. Note: ratings almost always greater than 10
• rating_denominator: denominator of the rating of a dog. Note: ratings almost always have a denominator of 10
• name: name of the dog
• doggo: one of the 4 dog "stage"
• floofer: one of the 4 dog "stage"
• pupper: one of the 4 dog "stage"
• puppo: one of the 4 dog "stage"

df_twitter_counts.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 3 columns):
tweet_id     2354 non-null object
retweets     2354 non-null object
favorites    2354 non-null object
dtypes: object(3)
memory usage: 55.2+ KB

df_img_pred.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
tweet_id    2075 non-null int64
jpg_url     2075 non-null object
img_num     2075 non-null int64
p1          2075 non-null object
p1_conf     2075 non-null float64
p1_dog      2075 non-null bool
p2          2075 non-null object
p2_conf     2075 non-null float64
p2_dog      2075 non-null bool
p3          2075 non-null object
p3_conf     2075 non-null float64
p3_dog      2075 non-null bool
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB

df_ing_pred is a table full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images)

• tweet_id is the last part of the tweet URL after "status/"
• p1 is the algorithm's #1 prediction for the image in the tweet
• p1_conf is how confident the algorithm is in its #1 prediction
• p1_dog is whether or not the #1 prediction is a breed of dog
• p2 is the algorithm's second most likely prediction
• p2_conf is how confident the algorithm is in its #2 prediction
• p2_dog is whether or not the #2 prediction is a breed of dog
• p3 is the algorithm's second most likely prediction
• p3_conf is how confident the algorithm is in its #3 prediction
• p3_dog is whether or not the #3 prediction is a breed of dog

# Check for nulls
df_twitter.isnull().sum()

tweet_id                      0   
in_reply_to_status_id         2278
in_reply_to_user_id           2278
timestamp                     0   
source                        0   
text                          0   
retweeted_status_id           2175
retweeted_status_user_id      2175
retweeted_status_timestamp    2175
expanded_urls                 59  
rating_numerator              0   
rating_denominator            0   
name                          0   
doggo                         0   
floofer                       0   
pupper                        0   
puppo                         0   
dtype: int64

df_twitter_counts.isnull().sum()

tweet_id     0
retweets     0
favorites    0
dtype: int64

df_img_pred.isnull().sum()

tweet_id    0
jpg_url     0
img_num     0
p1          0
p1_conf     0
p1_dog      0
p2          0
p2_conf     0
p2_dog      0
p3          0
p3_conf     0
p3_dog      0
dtype: int64

# Get some statistical insight of the dataset
df_twitter.describe()

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	retweeted_status_id	retweeted_status_user_id	rating_numerator	rating_denominator
count	2.356000e+03	7.800000e+01	7.800000e+01	1.810000e+02	1.810000e+02	2356.000000	2356.000000
mean	7.427716e+17	7.455079e+17	2.014171e+16	7.720400e+17	1.241698e+16	13.126486	10.455433
std	6.856705e+16	7.582492e+16	1.252797e+17	6.236928e+16	9.599254e+16	45.876648	6.745237
min	6.660209e+17	6.658147e+17	1.185634e+07	6.661041e+17	7.832140e+05	0.000000	0.000000
25%	6.783989e+17	6.757419e+17	3.086374e+08	7.186315e+17	4.196984e+09	10.000000	10.000000
50%	7.196279e+17	7.038708e+17	4.196984e+09	7.804657e+17	4.196984e+09	11.000000	10.000000
75%	7.993373e+17	8.257804e+17	4.196984e+09	8.203146e+17	4.196984e+09	12.000000	10.000000
max	8.924206e+17	8.862664e+17	8.405479e+17	8.874740e+17	7.874618e+17	1776.000000	170.000000

Rating numerator:

• mean: 13.126486
• min: 0.000000
• max: 1776.000000 (this might be an error)

Rating denominator:

• mean: 10.455433
• min: 0.000000
• max: 170.000000 (this might be an error)

# How many retweets
sum(~df_twitter.retweeted_status_id.isnull())

181

# How many replies
sum(~df_twitter.in_reply_to_status_id.isnull())

78

# Check for duplicated twitter ids within the same dataframe
sum(df_twitter.tweet_id.duplicated()) 

0

sum(df_img_pred.tweet_id.duplicated())

0

sum(df_twitter_counts.tweet_id.duplicated())

0

# Check for unique values
df_twitter.nunique()

tweet_id                      2356
in_reply_to_status_id         77  
in_reply_to_user_id           31  
timestamp                     2356
source                        4   
text                          2356
retweeted_status_id           181 
retweeted_status_user_id      25  
retweeted_status_timestamp    181 
expanded_urls                 2218
rating_numerator              40  
rating_denominator            18  
name                          957 
doggo                         2   
floofer                       2   
pupper                        2   
puppo                         2   
dtype: int64

df_img_pred.nunique()

tweet_id    2075
jpg_url     2009
img_num     4   
p1          378 
p1_conf     2006
p1_dog      2   
p2          405 
p2_conf     2004
p2_dog      2   
p3          408 
p3_conf     2006
p3_dog      2   
dtype: int64

df_twitter_counts.nunique()

tweet_id     2354
retweets     1724
favorites    2007
dtype: int64

# Check for the different dog names in the twitter dataframe
df_twitter.name.value_counts()

None        745
a           55 
Charlie     12 
Oliver      11 
Lucy        11 
Cooper      11 
Lola        10 
Penny       10 
Tucker      10 
Winston     9  
Bo          9  
Sadie       8  
the         8  
Daisy       7  
an          7  
Toby        7  
Buddy       7  
Bailey      7  
Koda        6  
Jack        6  
Leo         6  
Dave        6  
Bella       6  
Jax         6  
Scout       6  
Milo        6  
Oscar       6  
Stanley     6  
Rusty       6  
George      5  
           ..  
Jay         1  
Brockly     1  
Olaf        1  
Marty       1  
Darrel      1  
Kevon       1  
Kloey       1  
Robin       1  
Blanket     1  
O           1  
Tuck        1  
Jo          1  
Iggy        1  
Carper      1  
Liam        1  
Bode        1  
Dallas      1  
Zooey       1  
Brady       1  
Shikha      1  
Banditt     1  
Stefan      1  
Lillie      1  
Tom         1  
Tove        1  
Winifred    1  
Simba       1  
Craig       1  
Goliath     1  
Livvie      1  
Name: name, Length: 957, dtype: int64

As we can already see there are some invalid names such as "None", "a", "an" or "By",let's take a deepest investigation on the name column and sort them by its first letter:

# Chech how many names are marked as "None"
len(df_twitter[df_twitter['name'] == "None"])

745

# Chech how many names are marked as "None"
df_twitter[(df_twitter['name']=='None')].text

5       Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh   
7       When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq                       
12      Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm                                   
24      You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV                                                     
25      This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp            
30      @NonWhiteHat @MayhewMayhem omg hello tanner you are a scary good boy 12/10 would pet with extreme caution                                                            
32      RT @Athletics: 12/10 #BATP https://t.co/WxwJmvjfxo                                                                                                                   
35      I have a new hero and his name is Howard. 14/10 https://t.co/gzLHboL7Sk                                                                                              
37      Here we have a corgi undercover as a malamute. Pawbably doing important investigative work. Zero control over tongue happenings. 13/10 https://t.co/44ItaMubBf       
41      I present to you, Pup in Hat. Pup in Hat is great for all occasions. Extremely versatile. Compact as h*ck. 14/10 (IG: itselizabethgales) https://t.co/vvBOcC2VdC     
42      OMG HE DIDN'T MEAN TO HE WAS JUST TRYING A LITTLE BARKOUR HE'S SUPER SORRY 13/10 WOULD FORGIVE IMMEDIATE https://t.co/uF3pQ8Wubj                                     
47      Please only send dogs. We don't rate mechanics, no matter how h*ckin good. Thank you... 13/10 would sneak a pat https://t.co/Se5fZ9wp5E                              
55      @roushfenway These are good dogs but 17/10 is an emotional impulse rating. More like 13/10s                                                                          
59      Ugh not again. We only rate dogs. Please don't send in well-dressed  floppy-tongued street penguins. Dogs only please. Thank you... 12/10 https://t.co/WiAMbTkDPf    
62      Please don't send in photos without dogs in them. We're not @porch_rates. Insubordinate and churlish. Pretty good porch tho 11/10 https://t.co/HauE8M3Bu4            
64      @RealKentMurphy 14/10 confirmed                                                                                                                                      
72      Martha is stunning how h*ckin dare you. 13/10 https://t.co/9uABQXgjwa                                                                                                
78      RT @rachel2195: @dog_rates the boyfriend and his soaking wet pupper h*cking love his new hat 14/10 https://t.co/dJx4Gzc50G                                           
83      I can say with the pupmost confidence that the doggos who assisted with this search are heroic as h*ck. 14/10 for all https://t.co/8yoc1CNTsu                        
88      You'll get your package when that precious man is done appreciating the pups. 13/10 for everyone https://t.co/PFp4MghzBW                                             
89      Guys please stop sending pictures without any dogs in th- oh never mind hello excuse me sir. 12/10 stealthy as h*ck https://t.co/brCQoqc8AW                          
93      I can't believe this keeps happening. This, is a birb taking a bath. We only rate dogs. Please only send dogs. Thank you... 12/10 https://t.co/pwY9PQhtP2            
96      We usually don't rate Deck-bound Saskatoon Black Bears, but this one is h*ckin flawless. Sneaky tongue slip too. 13/10 would hug firmly https://t.co/mNuMH9400n      
99      Here's a very large dog. He has a date later. Politely asked this water person to check if his breath is bad. 12/10 good to go doggo https://t.co/EMYIdoblMR         
100     Here are my favorite #dogsatpollingstations \nMost voted for a more consistent walking schedule and to increase daily pats tenfold. All 13/10 https://t.co/17FVMl4VZ5
101     RT @loganamnosis: Penelope here is doing me quite a divertir. Well done, @dog_rates! Loving the pupdate. 14/10, je jouerais de nouveau. htt…                         
103     We. Only. Rate. Dogs. Do not send in other things like this fluffy floor shark clearly ready to attack. Get it together guys... 12/10 https://t.co/BZHiKx3FpQ        
110     Never doubt a doggo 14/10 https://t.co/AbBLh2FZCH                                                                                                                    
112     Real funny guys. Sending in a pic without a dog in it. Hilarious. We'll rate the rug tho because it's giving off a very good vibe. 11/10 https://t.co/GCD1JccCyi     
113     @ComplicitOwl @ShopWeRateDogs >10/10 is reserved for dogs                                                                                                         
                                    ...                                                                                                                                      
2294    Extremely intelligent dog here. Has learned to walk like human. Even has his own dog. Very impressive 10/10 https://t.co/0DvHAMdA4V                                  
2298    After much debate this dog is being upgraded to 10/10. I repeat 10/10                                                                                                
2299    Here we have a Hufflepuff. Loves vest. Eyes wide af. Flaccid tail. Matches carpet. Always a little blurry. 8/10 https://t.co/7JdgVqDnvR                              
2301    12/10 gimme now https://t.co/QZAnwgnOMB                                                                                                                              
2305    My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h                           
2306    These are Peruvian Feldspars. Their names are Cupit and Prencer. Both resemble Rand Paul. Sick outfits 10/10 & 10/10 https://t.co/ZnEMHBsAs1                     
2307    12/10 simply brilliant pup https://t.co/V6ZzG45zzG                                                                                                                   
2310    Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S                          
2316    Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s                           
2320    Here we see a lone northeastern Cumberbatch. Half ladybug. Only builds with bricks. Very confident with body. 7/10 https://t.co/7LtjBS0GPK                           
2321    "Can you behave? You're ruining my wedding day"\nDOG: idgaf this flashlight tastes good as hell\n\n10/10 https://t.co/GlFZPzqcEU                                     
2322    Oh boy what a pup! Sunglasses take this one to the next level. Weirdly folds front legs. Pretty big. 6/10 https://t.co/yECbFrSArM                                    
2323    Here we have an Austrian Pulitzer. Collectors edition. Levitates (?). 7/10 would garden with https://t.co/NMQq6HIglK                                                 
2324    *internally screaming* 12/10 https://t.co/YMcrXC2Y6R                                                                                                                 
2328    Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE                            
2329    Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl                                                                                    
2330    Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM                         
2331    Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO                              
2332    Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB                                                     
2336    Can take selfies 11/10 https://t.co/ws2AMaNwPW                                                                                                                       
2337    Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk                                                                                   
2338    Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX                               
2339    Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq                                     
2340    Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C                         
2341    This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP                                 
2342    Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7                                        
2343    Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY                            
2344    Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx                            
2351    Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq                                             
2355    Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj                                  
Name: text, Length: 745, dtype: object

np.sort(df_twitter.name.unique())

array(['Abby', 'Ace', 'Acro', 'Adele', 'Aiden', 'Aja', 'Akumi', 'Al',
       'Albert', 'Albus', 'Aldrick', 'Alejandro', 'Alexander',
       'Alexanderson', 'Alf', 'Alfie', 'Alfy', 'Alice', 'Amber', 'Ambrose',
       'Amy', 'Amélie', 'Anakin', 'Andru', 'Andy', 'Angel', 'Anna',
       'Anthony', 'Antony', 'Apollo', 'Aqua', 'Archie', 'Arlen', 'Arlo',
       'Arnie', 'Arnold', 'Arya', 'Ash', 'Asher', 'Ashleigh', 'Aspen',
       'Astrid', 'Atlas', 'Atticus', 'Aubie', 'Augie', 'Autumn', 'Ava',
       'Axel', 'Bailey', 'Baloo', 'Balto', 'Banditt', 'Banjo', 'Barclay',
       'Barney', 'Baron', 'Barry', 'Batdog', 'Bauer', 'Baxter', 'Bayley',
       'BeBe', 'Bear', 'Beau', 'Beckham', 'Beebop', 'Beemo', 'Bell',
       'Bella', 'Belle', 'Ben', 'Benedict', 'Benji', 'Benny', 'Bentley',
       'Berb', 'Berkeley', 'Bernie', 'Bert', 'Bertson', 'Betty', 'Beya',
       'Biden', 'Bilbo', 'Billl', 'Billy', 'Binky', 'Birf', 'Bisquick',
       'Blakely', 'Blanket', 'Blipson', 'Blitz', 'Bloo', 'Bloop', 'Blu',
       'Blue', 'Bluebert', 'Bo', 'Bob', 'Bobb', 'Bobbay', 'Bobble',
       'Bobby', 'Bode', 'Bodie', 'Bonaparte', 'Bones', 'Bookstore',
       'Boomer', 'Boots', 'Boston', 'Bowie', 'Brad', 'Bradlay', 'Bradley',
       'Brady', 'Brandi', 'Brandonald', 'Brandy', 'Brat', 'Brian',
       'Brockly', 'Brody', 'Bronte', 'Brooks', 'Brownie', 'Bruce',
       'Brudge', 'Bruiser', 'Bruno', 'Brutus', 'Bubba', 'Bubbles',
       'Buckley', 'Buddah', 'Buddy', 'Bungalo', 'Burt', 'Butter',
       'Butters', 'Cal', 'Calbert', 'Cali', 'Callie', 'Calvin', 'Canela',
       'Cannon', 'Carbon', 'Carl', 'Carll', 'Carly', 'Carper', 'Carter',
       'Caryl', 'Cash', 'Cassie', 'CeCe', 'Cecil', 'Cedrick', 'Cermet',
       'Chadrick', 'Champ', 'Charl', 'Charles', 'Charleson', 'Charlie',
       'Chase', 'Chaz', 'Cheesy', 'Chef', 'Chelsea', 'Cheryl', 'Chesney',
       'Chester', 'Chesterson', 'Chet', 'Chevy', 'Chip', 'Chipson',
       'Chloe', 'Chompsky', 'Christoper', 'Chubbs', 'Chuck', 'Chuckles',
       'Chuq', 'Churlie', 'Cilantro', 'Clarence', 'Clark', 'Clarkus',
       'Clarq', 'Claude', 'Cleopatricia', 'Clifford', 'Clybe', 'Clyde',
       'Coco', 'Cody', 'Colby', 'Coleman', 'Colin', 'Combo', 'Comet',
       'Cooper', 'Coops', 'Coopson', 'Cora', 'Corey', 'Covach', 'Craig',
       'Crawford', 'Creg', 'Crimson', 'Crouton', 'Crumpet', 'Crystal',
       'Cuddles', 'Cupcake', 'Cupid', 'Curtis', 'Daisy', 'Dakota', 'Dale',
       'Dallas', 'Damon', 'Daniel', 'Danny', 'Dante', 'Darby', 'Darla',
       'Darrel', 'Dash', 'Dave', 'Davey', 'Dawn', 'DayZ', 'Deacon',
       'Derby', 'Derek', 'Devón', 'Dewey', 'Dex', 'Dexter', 'Dido',
       'Dietrich', 'Diogi', 'Divine', 'Dixie', 'Django', 'Dobby', 'Doc',
       'DonDon', 'Donny', 'Doobert', 'Dook', 'Dot', 'Dotsy', 'Doug',
       'Duchess', 'Duddles', 'Dudley', 'Dug', 'Duke', 'Dunkin', 'Durg',
       'Dutch', 'Dwight', 'Dylan', 'Earl', 'Eazy', 'Ebby', 'Ed', 'Edd',
       'Edgar', 'Edmund', 'Eevee', 'Einstein', 'Eleanor', 'Eli', 'Ellie',
       'Elliot', 'Emanuel', 'Ember', 'Emma', 'Emmie', 'Emmy', 'Enchilada',
       'Erik', 'Eriq', 'Ester', 'Eugene', 'Eve', 'Evy', 'Fabio', 'Farfle',
       'Ferg', 'Fido', 'Fiji', 'Fillup', 'Filup', 'Finley', 'Finn',
       'Finnegus', 'Fiona', 'Fizz', 'Flash', 'Fletcher', 'Florence',
       'Flurpson', 'Flávio', 'Frank', 'Frankie', 'Franklin', 'Franq',
       'Fred', 'Freddery', 'Frönq', 'Furzey', 'Fwed', 'Fynn', 'Gabby',
       'Gabe', 'Gary', 'General', 'Genevieve', 'Geno', 'Geoff', 'George',
       'Georgie', 'Gerald', 'Gerbald', 'Gert', 'Gidget', 'Gilbert', 'Gin',
       'Ginger', 'Gizmo', 'Glacier', 'Glenn', 'Godi', 'Godzilla',
       'Goliath', 'Goose', 'Gordon', 'Grady', 'Grey', 'Griffin',
       'Griswold', 'Grizz', 'Grizzie', 'Grizzwald', 'Gromit', 'Gunner',
       'Gus', 'Gustaf', 'Gustav', 'Gòrdón', 'Hall', 'Halo', 'Hammond',
       'Hamrick', 'Hank', 'Hanz', 'Happy', 'Harlso', 'Harnold', 'Harold',
       'Harper', 'Harrison', 'Harry', 'Harvey', 'Hazel', 'Hector',
       'Heinrich', 'Henry', 'Herald', 'Herb', 'Hercules', 'Herm',
       'Hermione', 'Hero', 'Herschel', 'Hobbes', 'Holly', 'Horace',
       'Howie', 'Hubertson', 'Huck', 'Humphrey', 'Hunter', 'Hurley',
       'Huxley', 'Iggy', 'Ike', 'Indie', 'Iroh', 'Ito', 'Ivar', 'Izzy',
       'JD', 'Jack', 'Jackie', 'Jackson', 'Jameson', 'Jamesy', 'Jangle',
       'Jareld', 'Jarod', 'Jarvis', 'Jaspers', 'Jax', 'Jay', 'Jaycob',
       'Jazz', 'Jazzy', 'Jeb', 'Jebberson', 'Jed', 'Jeffrey', 'Jeffri',
       'Jeffrie', 'Jennifur', 'Jeph', 'Jeremy', 'Jerome', 'Jerry',
       'Jersey', 'Jesse', 'Jessifer', 'Jessiga', 'Jett', 'Jim', 'Jimbo',
       'Jiminus', 'Jiminy', 'Jimison', 'Jimothy', 'Jo', 'Jockson', 'Joey',
       'Jomathan', 'Jonah', 'Jordy', 'Josep', 'Joshwa', 'Juckson', 'Julio',
       'Julius', 'Juno', 'Kaia', 'Kaiya', 'Kallie', 'Kane', 'Kanu', 'Kara',
       'Karl', 'Karll', 'Karma', 'Kathmandu', 'Katie', 'Kawhi', 'Kayla',
       'Keet', 'Keith', 'Kellogg', 'Ken', 'Kendall', 'Kenneth', 'Kenny',
       'Kenzie', 'Keurig', 'Kevin', 'Kevon', 'Kial', 'Kilo', 'Kingsley',
       'Kirby', 'Kirk', 'Klein', 'Klevin', 'Kloey', 'Kobe', 'Koda', 'Kody',
       'Koko', 'Kollin', 'Kona', 'Kota', 'Kramer', 'Kreg', 'Kreggory',
       'Kulet', 'Kuyu', 'Kyle', 'Kyro', 'Lacy', 'Laela', 'Laika',
       'Lambeau', 'Lance', 'Larry', 'Lassie', 'Layla', 'Leela', 'Lennon',
       'Lenny', 'Lenox', 'Leo', 'Leonard', 'Leonidas', 'Levi', 'Liam',
       'Lilah', 'Lili', 'Lilli', 'Lillie', 'Lilly', 'Lily', 'Lincoln',
       'Linda', 'Link', 'Linus', 'Lipton', 'Livvie', 'Lizzie', 'Logan',
       'Loki', 'Lola', 'Lolo', 'Longfellow', 'Loomis', 'Lorelei',
       'Lorenzo', 'Lou', 'Louie', 'Louis', 'Luca', 'Lucia', 'Lucky',
       'Lucy', 'Lugan', 'Lulu', 'Luna', 'Lupe', 'Luther', 'Mabel', 'Mac',
       'Mack', 'Maddie', 'Maggie', 'Mairi', 'Maisey', 'Major', 'Maks',
       'Malcolm', 'Malikai', 'Margo', 'Mark', 'Marlee', 'Marley', 'Marq',
       'Marty', 'Marvin', 'Mary', 'Mason', 'Mattie', 'Maude', 'Mauve',
       'Max', 'Maxaroni', 'Maximus', 'Maxwell', 'Maya', 'Meatball',
       'Meera', 'Meyer', 'Mia', 'Michelangelope', 'Miguel', 'Mike',
       'Miley', 'Milky', 'Millie', 'Milo', 'Mimosa', 'Mingus', 'Mister',
       'Misty', 'Mitch', 'Mo', 'Moe', 'Mojo', 'Mollie', 'Molly', 'Mona',
       'Monkey', 'Monster', 'Monty', 'Moofasa', 'Mookie', 'Moose',
       'Moreton', 'Mosby', 'Murphy', 'Mutt', 'Mya', 'Nala', 'Naphaniel',
       'Napolean', 'Nelly', 'Neptune', 'Newt', 'Nico', 'Nida', 'Nigel',
       'Nimbus', 'Noah', 'Nollie', 'None', 'Noosh', 'Norman', 'Nugget',
       'O', 'Oakley', 'Obi', 'Obie', 'Oddie', 'Odie', 'Odin', 'Olaf',
       'Ole', 'Olive', 'Oliver', 'Olivia', 'Oliviér', 'Ollie', 'Opal',
       'Opie', 'Oreo', 'Orion', 'Oscar', 'Oshie', 'Otis', 'Ozzie', 'Ozzy',
       'Pablo', 'Paisley', 'Pancake', 'Panda', 'Patch', 'Patrick', 'Paull',
       'Pavlov', 'Pawnd', 'Peaches', 'Peanut', 'Penelope', 'Penny',
       'Pepper', 'Percy', 'Perry', 'Pete', 'Petrick', 'Pherb', 'Phil',
       'Philbert', 'Philippe', 'Phineas', 'Phred', 'Pickles', 'Pilot',
       'Pinot', 'Pip', 'Piper', 'Pippa', 'Pippin', 'Pipsy', 'Pluto',
       'Poppy', 'Pubert', 'Puff', 'Pumpkin', 'Pupcasso', 'Quinn', 'Ralf',
       'Ralph', 'Ralpher', 'Ralphie', 'Ralphson', 'Ralphus', 'Ralphy',
       'Ralphé', 'Rambo', 'Randall', 'Raphael', 'Rascal', 'Raymond',
       'Reagan', 'Reese', 'Reggie', 'Reginald', 'Remington', 'Remus',
       'Remy', 'Reptar', 'Rey', 'Rhino', 'Richie', 'Ricky', 'Ridley',
       'Riley', 'Rilo', 'Rinna', 'River', 'Rizzo', 'Rizzy', 'Robin',
       'Rocco', 'Rocky', 'Rodman', 'Rodney', 'Rolf', 'Romeo', 'Ron',
       'Ronduh', 'Ronnie', 'Rontu', 'Rooney', 'Roosevelt', 'Rorie', 'Rory',
       'Roscoe', 'Rose', 'Rosie', 'Rover', 'Rubio', 'Ruby', 'Rudy',
       'Rueben', 'Ruffles', 'Rufio', 'Rufus', 'Rumble', 'Rumpole',
       'Rupert', 'Rusty', 'Sadie', 'Sage', 'Sailer', 'Sailor', 'Sam',
       'Sammy', 'Sampson', 'Samsom', 'Samson', 'Sandra', 'Sandy', 'Sansa',
       'Sarge', 'Saydee', 'Schnitzel', 'Schnozz', 'Scooter', 'Scott',
       'Scout', 'Scruffers', 'Seamus', 'Sebastian', 'Sephie', 'Severus',
       'Shadoe', 'Shadow', 'Shaggy', 'Shakespeare', 'Shawwn', 'Shelby',
       'Shikha', 'Shiloh', 'Shnuggles', 'Shooter', 'Siba', 'Sid', 'Sierra',
       'Simba', 'Skittle', 'Skittles', 'Sky', 'Skye', 'Smiley', 'Smokey',
       'Snickers', 'Snicku', 'Snoop', 'Snoopy', 'Sobe', 'Socks',
       'Sojourner', 'Solomon', 'Sonny', 'Sophie', 'Sora', 'Spanky',
       'Spark', 'Sparky', 'Spencer', 'Sprinkles', 'Sprout', 'Staniel',
       'Stanley', 'Stark', 'Stefan', 'Stella', 'Stephan', 'Stephanus',
       'Steve', 'Steven', 'Stewie', 'Storkson', 'Stormy', 'Strider',
       'Striker', 'Strudel', 'Stu', 'Stuart', 'Stubert', 'Sugar', 'Suki',
       'Sully', 'Sundance', 'Sunny', 'Sunshine', 'Superpup', 'Swagger',
       'Sweet', 'Sweets', 'Taco', 'Tango', 'Tanner', 'Tassy', 'Tater',
       'Tayzie', 'Taz', 'Tebow', 'Ted', 'Tedders', 'Teddy', 'Tedrick',
       'Terrance', 'Terrenth', 'Terry', 'Tess', 'Tessa', 'Theo',
       'Theodore', 'Thor', 'Thumas', 'Tiger', 'Tilly', 'Timber', 'Timison',
       'Timmy', 'Timofy', 'Tino', 'Titan', 'Tito', 'Tobi', 'Toby', 'Todo',
       'Toffee', 'Tom', 'Tommy', 'Tonks', 'Torque', 'Tove', 'Travis',
       'Traviss', 'Trevith', 'Trigger', 'Trip', 'Tripp', 'Trooper', 'Tuck',
       'Tucker', 'Tuco', 'Tug', 'Tupawc', 'Tycho', 'Tyr', 'Tyrone',
       'Tyrus', 'Ulysses', 'Venti', 'Vince', 'Vincent', 'Vinnie',
       'Vinscent', 'Vixen', 'Wafer', 'Waffles', 'Walker', 'Wallace',
       'Wally', 'Walter', 'Watson', 'Wesley', 'Wiggles', 'Willem',
       'William', 'Willie', 'Willow', 'Willy', 'Wilson', 'Winifred',
       'Winnie', 'Winston', 'Wishes', 'Wyatt', 'Yoda', 'Yogi', 'Yukon',
       'Zara', 'Zeek', 'Zeke', 'Zeus', 'Ziva', 'Zoe', 'Zoey', 'Zooey',
       'Zuzu', 'a', 'actually', 'all', 'an', 'by', 'getting', 'his',
       'incredibly', 'infuriating', 'just', 'life', 'light', 'mad', 'my',
       'not', 'officially', 'old', 'one', 'quite', 'space', 'such', 'the',
       'this', 'unacceptable', 'very'], dtype=object)

There are some other error names: 'a', 'actually', 'all', 'an', 'by', 'getting', 'his', 'incredibly', 'infuriating', 'just', 'life', 'light', 'mad', 'my', 'not', 'officially', 'old', 'one', 'quite', 'space', 'such', 'the', 'this', 'unacceptable' and 'very'.

# Check how many name starting with a lowercase
len(df_twitter.loc[(df_twitter['name'].str.islower())])

109

df_twitter.loc[(df_twitter['name'].str.islower())].loc[:,['name','text']]

	name	text
22	such	I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba
56	a	Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF
118	quite	RT @dog_rates: We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10…
169	quite	We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10 https://t.co/g2nSyGenG9
193	quite	Guys, we only rate dogs. This is quite clearly a bulbasaur. Please only send dogs. Thank you... 12/10 human used pet, it's super effective https://t.co/Xc7uj1C64x
335	not	There's going to be a dog terminal at JFK Airport. This is not a drill. 10/10 \nhttps://t.co/dp5h9bCwU7
369	one	Occasionally, we're sent fantastic stories. This is one of them. 14/10 for Grace https://t.co/bZ4axuH6OK
542	incredibly	We only rate dogs. Please stop sending in non-canines like this Freudian Poof Lion. This is incredibly frustrating... 11/10 https://t.co/IZidSrBvhi
649	a	Here is a perfect example of someone who has their priorities in order. 13/10 for both owner and Forrest https://t.co/LRyMrU7Wfq
682	mad	RT @dog_rates: Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX
759	an	RT @dog_rates: This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR
773	very	RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://…
801	a	Guys this is getting so out of hand. We only rate dogs. This is a Galapagos Speed Panda. Pls only send dogs... 10/10 https://t.co/8lpAGaZRFn
819	very	We only rate dogs. Pls stop sending in non-canines like this Arctic Floof Kangaroo. This is very frustrating. 11/10 https://t.co/qlUDuPoE3d
822	just	RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC
852	my	This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB
924	one	This is one of the most inspirational stories I've ever come across. I have no words. 14/10 for both doggo and owner https://t.co/I5ld3eKD5k
988	not	What jokester sent in a pic without a dog in it? This is not @rock_rates. This is @dog_rates. Thank you ...10/10 https://t.co/nDPaYHrtNX
992	his	That is Quizno. This is his beach. He does not tolerate human shenanigans on his beach. 10/10 reclaim ur land doggo https://t.co/vdr7DaRSa7
993	one	This is one of the most reckless puppers I've ever seen. How she got a license in the first place is beyond me. 6/10 https://t.co/z5bAdtn9kd
1002	a	This is a mighty rare blue-tailed hammer sherk. Human almost lost a limb trying to take these. Be careful guys. 8/10 https://t.co/TGenMeXreW
1004	a	Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R
1017	a	This is a carrot. We only rate dogs. Please only send in dogs. You all really should know this by now ...11/10 https://t.co/9e48aPrBm2
1025	an	This is an Iraqi Speed Kangaroo. It is not a dog. Please only send in dogs. I'm very angry with all of you ...9/10 https://t.co/5qpBTTpgUt
1031	very	We only rate dogs. Pls stop sending in non-canines like this Jamaican Flop Seal. This is very very frustrating. 9/10 https://t.co/nc53zEN0hZ
1040	actually	This is actually a pupper and I'd pet it so well. 12/10\nhttps://t.co/RNqS7C4Y4N
1049	a	This is a very rare Great Alaskan Bush Pupper. Hard to stumble upon without spooking. 12/10 would pet passionately https://t.co/xOBKCdpzaa
1063	just	This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC
1071	getting	This is getting incredibly frustrating. This is a Mexican Golden Beaver. We only rate dogs. Only send dogs ...10/10 https://t.co/0yolOOyD3X
1095	mad	Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX
...	...	...
2191	a	This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5
2198	a	This is a wild Toblerone from Papua New Guinea. Mouth always open. Addicted to hay. Acts blind. 7/10 handsome dog https://t.co/IGmVbz07tZ
2204	an	This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg
2211	a	Here is a horned dog. Much grace. Can jump over moons (dam!). Paws not soft. Bad at barking. 7/10 can still pet tho https://t.co/2Su7gmsnZm
2212	the	Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3
2218	a	This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt
2222	a	Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m
2235	a	This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb
2249	a	This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba
2255	a	This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD
2264	a	This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\n9/10 https://t.co/IQTOMqDUIe
2273	a	This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk
2287	a	This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW
2304	a	This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc
2311	a	This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa
2314	a	This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB
2326	quite	This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t
2327	a	This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h
2333	an	This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL
2334	a	This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0
2335	an	This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv
2345	the	This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK
2346	the	Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p
2347	a	My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O
2348	a	Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt
2349	an	This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc
2350	a	This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe
2352	a	This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx
2353	a	Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR
2354	a	This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI

109 rows × 2 columns

Filtering by names starting with lowercase, looks like there is an error with the name caching algorithm as it expected a name after phrases like: "This is", "Here is", "that is" or "Say hello to". As we can see in the majority of the sample texts of each tweets, the name doesn't show. Nevertheless, for some other tweet's texts, we can see the real name after phrases such as: 'Her name is', 'His name is', 'That is' and 'named'.

• index number 852 - This is my dog. Her name is Zoey... (name detected by the algorithm: my)
• index number 992 - That is Quizno. This is his beach... (name detected by the algorithm: his)
• index number 2191 - This is a Slovakian Helter Skelter Feta named Leroi... (name detected by the algorithm: a)
• index number 2249 - This is a Shotokon Macadamia mix named Cheryl... (name detected by the algorithm: a)
• index number 2255 - This is a rare Hungarian Pinot named Jessiga ... (name detected by the algorithm: a)
• index number 2264 - This is a southwest Coriander named Klint... (name detected by the algorithm: a)
• index number 2273 - This is a northern Wahoo named Kohl... (name detected by the algorithm: a)
• index number 2287 - This is a Dasani Kingfisher from Maine. His name is Daryl... (name detected by the algorithm: a)
• index number 2304 - This is a curly Ticonderoga named Pepe... (name detected by the algorithm: a)
• index number 2311 - This is a purebred Bacardi named Octaviath... (name detected by the algorithm: a)
• index number 2314 - This is a This is a golden Buckminsterfullerene named Johm... (name detected by the algorithm: a)

# Check the doggo stage
df_twitter.doggo.value_counts()

None     2259
doggo    97  
Name: doggo, dtype: int64

# Check the floofer stage
df_twitter.floofer.value_counts()

None       2346
floofer    10  
Name: floofer, dtype: int64

# Check the puppo stage
df_twitter.puppo.value_counts()

None     2326
puppo    30  
Name: puppo, dtype: int64

# Check the pupper stage
df_twitter.pupper.value_counts()

None      2099
pupper    257 
Name: pupper, dtype: int64

# Check if there are records with more than one "dog stage" for the same tweet_id

# Doggo and floofer
print("Number of records with both doggo and floofer stage", len(df_twitter[(df_twitter.doggo != 'None') & (df_twitter.floofer != 'None')]))
# Doggo and pupper
print("Number of records with both doggo and pupper stage", len(df_twitter[(df_twitter.doggo != 'None') & (df_twitter.pupper != 'None')]))
# Doggo and puppo
print("Number of records with both doggo and puppo stage", len(df_twitter[(df_twitter.doggo != 'None') & (df_twitter.puppo != 'None')]))
# floofer and pupper
print("Number of records with both floofer and pupper stage", len(df_twitter[(df_twitter.floofer != 'None') & (df_twitter.pupper != 'None')]))
# floofer and puppo
print("Number of records with both floofer and puppo stage", len(df_twitter[(df_twitter.floofer != 'None') & (df_twitter.puppo != 'None')]))
# pupper and puppo
print("Number of records with both pupper and puppo stage", len(df_twitter[(df_twitter.pupper != 'None') & (df_twitter.puppo != 'None')]))

Number of records with both doggo and floofer stage 1
Number of records with both doggo and pupper stage 12
Number of records with both doggo and puppo stage 1
Number of records with both floofer and pupper stage 0
Number of records with both floofer and puppo stage 0
Number of records with both pupper and puppo stage 0

Let's look deeper into the records with more than one stage:

# Check the tweet text
df_twitter[(df_twitter.doggo != 'None') & (df_twitter.floofer != 'None')]

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
200	854010172552949760	NaN	NaN	2017-04-17 16:34:26 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk	NaN	NaN	NaN	https://twitter.com/dog_rates/status/854010172552949760/photo/1,https://twitter.com/dog_rates/status/854010172552949760/photo/1	11	10	None	doggo	floofer	None	None

As can be read in the text, this dog is a Floofer.

df_twitter[(df_twitter.doggo != 'None') & (df_twitter.pupper != 'None')]

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
460	817777686764523521	NaN	NaN	2017-01-07 16:59:28 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Dido. She's playing the lead role in "Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple." 13/10 (IG: didodoggo) https://t.co/m7isZrOBX7	NaN	NaN	NaN	https://twitter.com/dog_rates/status/817777686764523521/video/1	13	10	Dido	doggo	None	pupper	None
531	808106460588765185	NaN	NaN	2016-12-12 00:29:28 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Here we have Burke (pupper) and Dexter (doggo). Pupper wants to be exactly like doggo. Both 12/10 would pet at same time https://t.co/ANBpEYHaho	NaN	NaN	NaN	https://twitter.com/dog_rates/status/808106460588765185/photo/1	12	10	None	doggo	None	pupper	None
565	802265048156610565	7.331095e+17	4.196984e+09	2016-11-25 21:37:47 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Like doggo, like pupper version 2. Both 11/10 https://t.co/9IxWAXFqze	NaN	NaN	NaN	https://twitter.com/dog_rates/status/802265048156610565/photo/1	11	10	None	doggo	None	pupper	None
575	801115127852503040	NaN	NaN	2016-11-22 17:28:25 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj	NaN	NaN	NaN	https://twitter.com/dog_rates/status/801115127852503040/photo/1,https://twitter.com/dog_rates/status/801115127852503040/photo/1	12	10	Bones	doggo	None	pupper	None
705	785639753186217984	NaN	NaN	2016-10-11 00:34:48 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https://t.co/f2wmLZTPHd	NaN	NaN	NaN	https://twitter.com/dog_rates/status/785639753186217984/photo/1,https://twitter.com/dog_rates/status/785639753186217984/photo/1	10	10	Pinot	doggo	None	pupper	None
733	781308096455073793	NaN	NaN	2016-09-29 01:42:20 +0000	<a href="http://vine.co" rel="nofollow">Vine - Make a Scene</a>	Pupper butt 1, Doggo 0. Both 12/10 https://t.co/WQvcPEpH2u	NaN	NaN	NaN	https://vine.co/v/5rgu2Law2ut	12	10	None	doggo	None	pupper	None
778	775898661951791106	NaN	NaN	2016-09-14 03:27:11 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	RT @dog_rates: Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda	7.331095e+17	4.196984e+09	2016-05-19 01:38:16 +0000	https://twitter.com/dog_rates/status/733109485275860992/photo/1,https://twitter.com/dog_rates/status/733109485275860992/photo/1	12	10	None	doggo	None	pupper	None
822	770093767776997377	NaN	NaN	2016-08-29 03:00:36 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC	7.410673e+17	4.196984e+09	2016-06-10 00:39:48 +0000	https://twitter.com/dog_rates/status/741067306818797568/photo/1,https://twitter.com/dog_rates/status/741067306818797568/photo/1	12	10	just	doggo	None	pupper	None
889	759793422261743616	NaN	NaN	2016-07-31 16:50:42 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Meet Maggie &amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll	NaN	NaN	NaN	https://twitter.com/dog_rates/status/759793422261743616/photo/1,https://twitter.com/dog_rates/status/759793422261743616/photo/1	12	10	Maggie	doggo	None	pupper	None
956	751583847268179968	NaN	NaN	2016-07-09 01:08:47 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8	NaN	NaN	NaN	https://twitter.com/dog_rates/status/751583847268179968/photo/1	5	10	None	doggo	None	pupper	None
1063	741067306818797568	NaN	NaN	2016-06-10 00:39:48 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC	NaN	NaN	NaN	https://twitter.com/dog_rates/status/741067306818797568/photo/1	12	10	just	doggo	None	pupper	None
1113	733109485275860992	NaN	NaN	2016-05-19 01:38:16 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda	NaN	NaN	NaN	https://twitter.com/dog_rates/status/733109485275860992/photo/1	12	10	None	doggo	None	pupper	None

Looking at the text and also folowwing the link, I've determined if the tweets rate two dogs or one, getting for this last case its stage:

tweet_id	2 dogs / 1 dog (Stage)
817777686764523521	Pupper
808106460588765185	2 dogs
802265048156610565	2 dogs
801115127852503040	Doggo
785639753186217984	Doggo
781308096455073793	2 dogs
775898661951791106	2 dogs
770093767776997377	2 dogs
759793422261743616	2 dogs
751583847268179968	doggo
741067306818797568	2 dogs
733109485275860992	2 dogs

df_twitter[(df_twitter.doggo != 'None') & (df_twitter.puppo != 'None')]

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
191	855851453814013952	NaN	NaN	2017-04-22 18:31:02 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Here's a puppo participating in the #ScienceMarch. Cleverly disguising her own doggo agenda. 13/10 would keep the planet habitable for https://t.co/cMhq16isel	NaN	NaN	NaN	https://twitter.com/dog_rates/status/855851453814013952/photo/1	13	10	None	doggo	None	None	puppo

As can be read in the text, this dog is a Puppo.

# Check the different values for the numerator
df_twitter.rating_numerator.value_counts()

12      558
11      464
10      461
13      351
9       158
8       102
7       55 
14      54 
5       37 
6       32 
3       19 
4       17 
1       9  
2       9  
420     2  
0       2  
15      2  
75      2  
80      1  
20      1  
24      1  
26      1  
44      1  
50      1  
60      1  
165     1  
84      1  
88      1  
144     1  
182     1  
143     1  
666     1  
960     1  
1776    1  
17      1  
27      1  
45      1  
99      1  
121     1  
204     1  
Name: rating_numerator, dtype: int64

# Check the different values for the denominator
df_twitter.rating_denominator.value_counts()

10     2333
11     3   
50     3   
80     2   
20     2   
2      1   
16     1   
40     1   
70     1   
15     1   
90     1   
110    1   
120    1   
130    1   
150    1   
170    1   
7      1   
0      1   
Name: rating_denominator, dtype: int64

# Sort the numerator values
np.sort(df_twitter['rating_numerator'].unique())

array([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,   10,
         11,   12,   13,   14,   15,   17,   20,   24,   26,   27,   44,
         45,   50,   60,   75,   80,   84,   88,   99,  121,  143,  144,
        165,  182,  204,  420,  666,  960, 1776])

df_twitter[df_twitter['rating_numerator']>=26]

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
188	855862651834028034	8.558616e+17	1.943518e+08	2017-04-22 19:15:32 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	@dhmontgomery We also gave snoop dogg a 420/10 but I think that predated your research	NaN	NaN	NaN	NaN	420	10	None	None	None	None	None
189	855860136149123072	8.558585e+17	1.361572e+07	2017-04-22 19:05:32 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	@s8n You tried very hard to portray this good boy as not so good, but you have ultimately failed. His goodness shines through. 666/10	NaN	NaN	NaN	NaN	666	10	None	None	None	None	None
290	838150277551247360	8.381455e+17	2.195506e+07	2017-03-04 22:12:52 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	@markhoppus 182/10	NaN	NaN	NaN	NaN	182	10	None	None	None	None	None
313	835246439529840640	8.352460e+17	2.625958e+07	2017-02-24 21:54:03 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho	NaN	NaN	NaN	NaN	960	0	None	None	None	None	None
340	832215909146226688	NaN	NaN	2017-02-16 13:11:49 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	RT @dog_rates: This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wu…	7.867091e+17	4.196984e+09	2016-10-13 23:23:56 +0000	https://twitter.com/dog_rates/status/786709082849828864/photo/1	75	10	Logan	None	None	None	None
433	820690176645140481	NaN	NaN	2017-01-15 17:52:40 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd	NaN	NaN	NaN	https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1	84	70	None	None	None	None	None
695	786709082849828864	NaN	NaN	2016-10-13 23:23:56 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS	NaN	NaN	NaN	https://twitter.com/dog_rates/status/786709082849828864/photo/1	75	10	Logan	None	None	None	None
763	778027034220126208	NaN	NaN	2016-09-20 00:24:34 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq	NaN	NaN	NaN	https://twitter.com/dog_rates/status/778027034220126208/photo/1	27	10	Sophie	None	None	pupper	None
902	758467244762497024	NaN	NaN	2016-07-28 01:00:57 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE	NaN	NaN	NaN	https://twitter.com/dog_rates/status/758467244762497024/video/1	165	150	None	None	None	None	None
979	749981277374128128	NaN	NaN	2016-07-04 15:00:45 +0000	<a href="https://about.twitter.com/products/tweetdeck" rel="nofollow">TweetDeck</a>	This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh	NaN	NaN	NaN	https://twitter.com/dog_rates/status/749981277374128128/photo/1	1776	10	Atticus	None	None	None	None
1120	731156023742988288	NaN	NaN	2016-05-13 16:15:54 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv	NaN	NaN	NaN	https://twitter.com/dog_rates/status/731156023742988288/photo/1	204	170	this	None	None	None	None
1202	716439118184652801	NaN	NaN	2016-04-03 01:36:11 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq	NaN	NaN	NaN	https://twitter.com/dog_rates/status/716439118184652801/photo/1	50	50	Bluebert	None	None	None	None
1228	713900603437621249	NaN	NaN	2016-03-27 01:29:02 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1	NaN	NaN	NaN	https://twitter.com/dog_rates/status/713900603437621249/photo/1	99	90	None	None	None	None	None
1254	710658690886586372	NaN	NaN	2016-03-18 02:46:49 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12	NaN	NaN	NaN	https://twitter.com/dog_rates/status/710658690886586372/photo/1	80	80	None	None	None	None	None
1274	709198395643068416	NaN	NaN	2016-03-14 02:04:08 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	From left to right:\nCletus, Jerome, Alejandro, Burp, &amp; Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK	NaN	NaN	NaN	https://twitter.com/dog_rates/status/709198395643068416/photo/1	45	50	None	None	None	None	None
1351	704054845121142784	NaN	NaN	2016-02-28 21:25:30 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa	NaN	NaN	NaN	https://twitter.com/dog_rates/status/704054845121142784/photo/1	60	50	a	None	None	None	None
1433	697463031882764288	NaN	NaN	2016-02-10 16:51:59 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ	NaN	NaN	NaN	https://twitter.com/dog_rates/status/697463031882764288/photo/1	44	40	None	None	None	None	None
1634	684225744407494656	6.842229e+17	4.196984e+09	2016-01-05 04:11:44 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684225744407494656/photo/1,https://twitter.com/dog_rates/status/684225744407494656/photo/1	143	130	None	None	None	None	None
1635	684222868335505415	NaN	NaN	2016-01-05 04:00:18 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684222868335505415/photo/1	121	110	None	None	None	None	None
1712	680494726643068929	NaN	NaN	2015-12-25 21:06:00 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD	NaN	NaN	NaN	https://twitter.com/dog_rates/status/680494726643068929/photo/1	26	10	None	None	None	None	None
1779	677716515794329600	NaN	NaN	2015-12-18 05:06:23 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq	NaN	NaN	NaN	https://twitter.com/dog_rates/status/677716515794329600/photo/1	144	120	None	None	None	None	None
1843	675853064436391936	NaN	NaN	2015-12-13 01:41:41 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw	NaN	NaN	NaN	https://twitter.com/dog_rates/status/675853064436391936/photo/1,https://twitter.com/dog_rates/status/675853064436391936/photo/1	88	80	None	None	None	None	None
2074	670842764863651840	NaN	NaN	2015-11-29 05:52:33 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	After so many requests... here you go.\n\nGood dogg. 420/10 https://t.co/yfAAo1gdeY	NaN	NaN	NaN	https://twitter.com/dog_rates/status/670842764863651840/photo/1	420	10	None	None	None	None	None

There are severals errors due to numerators with decimals

# Sort the denominator values
np.sort(df_twitter['rating_denominator'].unique())

array([  0,   2,   7,  10,  11,  15,  16,  20,  40,  50,  70,  80,  90,
       110, 120, 130, 150, 170])

df_twitter.rating_denominator.value_counts()

10     2333
11     3   
50     3   
80     2   
20     2   
2      1   
16     1   
40     1   
70     1   
15     1   
90     1   
110    1   
120    1   
130    1   
150    1   
170    1   
7      1   
0      1   
Name: rating_denominator, dtype: int64

df_twitter[df_twitter['rating_denominator']!=10].loc[:,['text','rating_denominator']]

	text	rating_denominator
313	@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho	0
342	@docmisterio account started on 11/15/15	15
433	The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd	70
516	Meet Sam. She smiles 24/7 & secretly aspires to be a reindeer. \nKeep Sam smiling by clicking and sharing this link:\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx	7
784	RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…	11
902	Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE	150
1068	After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ	11
1120	Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv	170
1165	Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a	20
1202	This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq	50
1228	Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1	90
1254	Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12	80
1274	From left to right:\nCletus, Jerome, Alejandro, Burp, & Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK	50
1351	Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa	50
1433	Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ	40
1598	Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating	20
1634	Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3	130
1635	Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55	110
1662	This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5	11
1663	I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible	16
1779	IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq	120
1843	Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw	80
2335	This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv	2

df_twitter[df_twitter['rating_denominator']==50].loc[:,['text','rating_denominator']]

	text	rating_denominator
1202	This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq	50
1274	From left to right:\nCletus, Jerome, Alejandro, Burp, & Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK	50
1351	Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa	50

df_twitter.iloc[1165,:]

tweet_id                      722974582966214656                                                                
in_reply_to_status_id         NaN                                                                               
in_reply_to_user_id           NaN                                                                               
timestamp                     2016-04-21 02:25:47 +0000                                                         
source                        <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
text                          Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a                  
retweeted_status_id           NaN                                                                               
retweeted_status_user_id      NaN                                                                               
retweeted_status_timestamp    NaN                                                                               
expanded_urls                 https://twitter.com/dog_rates/status/722974582966214656/photo/1                   
rating_numerator              4                                                                                 
rating_denominator            20                                                                                
name                          None                                                                              
doggo                         None                                                                              
floofer                       None                                                                              
pupper                        None                                                                              
puppo                         None                                                                              
Name: 1165, dtype: object

As we can see in the text of this tweet above, the denominator of 20 is due to the rating of 2 dogs.

df_img_pred.columns

Index(['tweet_id', 'jpg_url', 'img_num', 'p1', 'p1_conf', 'p1_dog', 'p2',
       'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog'],
      dtype='object')

# Check for duplicated tweet_ids
sum(df_img_pred.tweet_id.duplicated())

0

# Check for duplicated tweets urls
sum(df_img_pred.jpg_url.duplicated())

66

# Check how many time are the jpg_urls duplicated
df_img_pred.jpg_url.value_counts()

https://pbs.twimg.com/media/CkNjahBXAAQ2kWo.jpg                                            2
https://pbs.twimg.com/ext_tw_video_thumb/807106774843039744/pu/img/8XZg1xW35Xp2J6JW.jpg    2
https://pbs.twimg.com/media/CuRDF-XWcAIZSer.jpg                                            2
https://pbs.twimg.com/media/Ct2qO5PXEAE6eB0.jpg                                            2
https://pbs.twimg.com/media/Cq9guJ5WgAADfpF.jpg                                            2
https://pbs.twimg.com/media/CtKHLuCWYAA2TTs.jpg                                            2
https://pbs.twimg.com/tweet_video_thumb/CeBym7oXEAEWbEg.jpg                                2
https://pbs.twimg.com/media/DA7iHL5U0AA1OQo.jpg                                            2
https://pbs.twimg.com/media/C4KHj-nWQAA3poV.jpg                                            2
https://pbs.twimg.com/media/CUN4Or5UAAAa5K4.jpg                                            2
https://pbs.twimg.com/media/CtzKC7zXEAALfSo.jpg                                            2
https://pbs.twimg.com/media/CsVO7ljW8AAckRD.jpg                                            2
https://pbs.twimg.com/media/CtVAvX-WIAAcGTf.jpg                                            2
https://pbs.twimg.com/media/Cx5R8wPVEAALa9r.jpg                                            2
https://pbs.twimg.com/media/CmoPdmHW8AAi8BI.jpg                                            2
https://pbs.twimg.com/media/CV_cnjHWUAADc-c.jpg                                            2
https://pbs.twimg.com/media/Ct72q9jWcAAhlnw.jpg                                            2
https://pbs.twimg.com/media/CiyHLocU4AI2pJu.jpg                                            2
https://pbs.twimg.com/media/C2kzTGxWEAEOpPL.jpg                                            2
https://pbs.twimg.com/media/ChK1tdBWwAQ1flD.jpg                                            2
https://pbs.twimg.com/media/Cp6db4-XYAAMmqL.jpg                                            2
https://pbs.twimg.com/media/CzG425nWgAAnP7P.jpg                                            2
https://pbs.twimg.com/media/Cbs3DOAXIAAp3Bd.jpg                                            2
https://pbs.twimg.com/media/C4bTH6nWMAAX_bJ.jpg                                            2
https://pbs.twimg.com/media/Ck2d7tJWUAEPTL3.jpg                                            2
https://pbs.twimg.com/media/CcG07BYW0AErrC9.jpg                                            2
https://pbs.twimg.com/media/CVMOlMiWwAA4Yxl.jpg                                            2
https://pbs.twimg.com/media/CkjMx99UoAM2B1a.jpg                                            2
https://pbs.twimg.com/ext_tw_video_thumb/817423809049493505/pu/img/5OFW0yueFu9oTUiQ.jpg    2
https://pbs.twimg.com/media/C2oRbOuWEAAbVSl.jpg                                            2
                                                                                          ..
https://pbs.twimg.com/media/CmAC7ehXEAAqSuW.jpg                                            1
https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg                                            1
https://pbs.twimg.com/media/CUTDtyGXIAARxus.jpg                                            1
https://pbs.twimg.com/media/CWofOHUWUAACGVa.jpg                                            1
https://pbs.twimg.com/media/CV1WXsmWcAAgQ56.jpg                                            1
https://pbs.twimg.com/media/Cmjlsh1XgAEvhq_.jpg                                            1
https://pbs.twimg.com/media/Ce99GhLW8AAHG38.jpg                                            1
https://pbs.twimg.com/media/Cj1I1fbWYAAOwff.jpg                                            1
https://pbs.twimg.com/media/CmyPXNOW8AEtaJ-.jpg                                            1
https://pbs.twimg.com/media/C61lFFiWoAAJdiL.jpg                                            1
https://pbs.twimg.com/media/CXh5_dDWQAIbU-J.jpg                                            1
https://pbs.twimg.com/media/C5O1UAaWIAAMBMd.jpg                                            1
https://pbs.twimg.com/media/CdecUSzUIAAHCvg.jpg                                            1
https://pbs.twimg.com/media/CVfwXuWWIAAqnoi.jpg                                            1
https://pbs.twimg.com/media/CZIJD2SWIAMJgNI.jpg                                            1
https://pbs.twimg.com/media/CkTvJTdXAAAEfbT.jpg                                            1
https://pbs.twimg.com/media/CVCIQX7UkAEzqh_.jpg                                            1
https://pbs.twimg.com/media/DAEfCFXUIAA1uqj.jpg                                            1
https://pbs.twimg.com/ext_tw_video_thumb/762471745303355393/pu/img/RKcEUz7-VDipoGKJ.jpg    1
https://pbs.twimg.com/media/CU3d0azWUAA38FD.jpg                                            1
https://pbs.twimg.com/media/C0jcmOKVQAAd0VR.jpg                                            1
https://pbs.twimg.com/media/CWntoDVWcAEl3NB.jpg                                            1
https://pbs.twimg.com/media/CwRN8H6WgAASe4X.jpg                                            1
https://pbs.twimg.com/media/C3iq0EEXUAAdBYC.jpg                                            1
https://pbs.twimg.com/media/ChJO9YaWYAEL0zC.jpg                                            1
https://pbs.twimg.com/media/DD-40X3WAAAJPU5.jpg                                            1
https://pbs.twimg.com/media/C6wbE5bXUAAh1Hv.jpg                                            1
https://pbs.twimg.com/media/DDMD_phXoAQ1qf0.jpg                                            1
https://pbs.twimg.com/media/CUrNmtFWoAAnWCD.jpg                                            1
https://pbs.twimg.com/media/CU8z65IUEAQBc4q.jpg                                            1
Name: jpg_url, Length: 2009, dtype: int64

# Check the different values for p1 and their frequency for each of them
df_img_pred.p1.value_counts()

golden_retriever             150
Labrador_retriever           100
Pembroke                     89 
Chihuahua                    83 
pug                          57 
chow                         44 
Samoyed                      43 
toy_poodle                   39 
Pomeranian                   38 
cocker_spaniel               30 
malamute                     30 
French_bulldog               26 
Chesapeake_Bay_retriever     23 
miniature_pinscher           23 
seat_belt                    22 
Siberian_husky               20 
Staffordshire_bullterrier    20 
German_shepherd              20 
web_site                     19 
Cardigan                     19 
Eskimo_dog                   18 
Shetland_sheepdog            18 
beagle                       18 
Maltese_dog                  18 
teddy                        18 
Shih-Tzu                     17 
Lakeland_terrier             17 
Rottweiler                   17 
kuvasz                       16 
Italian_greyhound            16 
                             .. 
carousel                     1  
sandbar                      1  
leopard                      1  
handkerchief                 1  
water_buffalo                1  
minibus                      1  
sunglasses                   1  
three-toed_sloth             1  
agama                        1  
bison                        1  
crash_helmet                 1  
padlock                      1  
groenendael                  1  
slug                         1  
toilet_seat                  1  
king_penguin                 1  
pitcher                      1  
EntleBucher                  1  
otter                        1  
ocarina                      1  
bighorn                      1  
desktop_computer             1  
espresso                     1  
cougar                       1  
rotisserie                   1  
hare                         1  
robin                        1  
clog                         1  
pencil_box                   1  
guenon                       1  
Name: p1, Length: 378, dtype: int64

df_img_pred.p2.value_counts()

Labrador_retriever                104
golden_retriever                  92 
Cardigan                          73 
Chihuahua                         44 
Pomeranian                        42 
Chesapeake_Bay_retriever          41 
French_bulldog                    41 
toy_poodle                        37 
cocker_spaniel                    34 
miniature_poodle                  33 
Siberian_husky                    33 
beagle                            28 
Eskimo_dog                        27 
Pembroke                          27 
collie                            27 
kuvasz                            26 
Italian_greyhound                 22 
American_Staffordshire_terrier    21 
Pekinese                          21 
malinois                          20 
Samoyed                           20 
miniature_pinscher                20 
chow                              20 
toy_terrier                       20 
Norwegian_elkhound                19 
Boston_bull                       19 
Staffordshire_bullterrier         18 
Irish_terrier                     17 
pug                               17 
kelpie                            16 
                                  .. 
African_hunting_dog               1  
can_opener                        1  
dugong                            1  
lampshade                         1  
bucket                            1  
snail                             1  
Gila_monster                      1  
shower_curtain                    1  
hand-held_computer                1  
birdhouse                         1  
Japanese_spaniel                  1  
grey_whale                        1  
standard_schnauzer                1  
jigsaw_puzzle                     1  
European_gallinule                1  
window_shade                      1  
grey_fox                          1  
bow                               1  
trench_coat                       1  
iPod                              1  
minibus                           1  
Kerry_blue_terrier                1  
dining_table                      1  
cliff                             1  
turnstile                         1  
mosquito_net                      1  
tick                              1  
common_newt                       1  
bagel                             1  
shower_cap                        1  
Name: p2, Length: 405, dtype: int64

df_img_pred.p3.value_counts()

Labrador_retriever                79
Chihuahua                         58
golden_retriever                  48
Eskimo_dog                        38
kelpie                            35
kuvasz                            34
chow                              32
Staffordshire_bullterrier         32
cocker_spaniel                    31
beagle                            31
Pomeranian                        29
Pekinese                          29
toy_poodle                        29
Pembroke                          27
Great_Pyrenees                    27
Chesapeake_Bay_retriever          27
malamute                          26
French_bulldog                    26
American_Staffordshire_terrier    24
pug                               23
Cardigan                          23
basenji                           21
toy_terrier                       20
bull_mastiff                      20
Siberian_husky                    19
Boston_bull                       17
Shetland_sheepdog                 17
boxer                             16
doormat                           16
Lakeland_terrier                  16
                                  ..
barbell                           1 
gar                               1 
theater_curtain                   1 
wild_boar                         1 
mitten                            1 
grand_piano                       1 
bow                               1 
balance_beam                      1 
bonnet                            1 
Kerry_blue_terrier                1 
hammerhead                        1 
mongoose                          1 
cliff                             1 
mosquito_net                      1 
American_black_bear               1 
cuirass                           1 
common_newt                       1 
loupe                             1 
grocery_store                     1 
can_opener                        1 
chain                             1 
banana                            1 
coral_reef                        1 
whiptail                          1 
bullfrog                          1 
wok                               1 
go-kart                           1 
tiger_cat                         1 
guillotine                        1 
shower_cap                        1 
Name: p3, Length: 408, dtype: int64

# Check for duplicated tweet_ids
sum(df_twitter_counts.tweet_id.duplicated())

0

Data Inclusion Criteria

In order to analyze the data, I followed the criteria found below:

• Retweets must not be included
• Replies must not be included

Quality Issues

The four main data quality dimensions are:

• Completeness: missing data
• Validity: if the data make sense
• Accuracy: inaccurate data (wrong data can still show up as valid)
• Consistency: standardization

DataFrame: df_twitter

• Q1. Text column contains truncated text instead of displayable text.
• Q2. The data contains retweets and replies and I only want to investigate original tweet's ratings.
• Q3. There are some columns in this dataframe that I won't be using for my analisys.
• Q4. There are some invalid names such as 'a', 'actually', 'all', 'an', 'by', etc.
• Q5. Erroneous datatypes: tweet_id, timestamp.
• Q6. Some records have more than one dog stage, due to detection errors and the fact that some tweets rate 2 dogs.
• Q7. For several columns, nulls are marked as None.
• Q8. There are several tweets with a denominator different to 10, might be related to more than just 1 dog per tweet.
• Q9. There are several tweets with really high values for the numerators (e.g.: from 100 to 1776) and numerators with decimals.

DataFrame: df_img_pred

• Q5. Erroneous datatypes: tweet_id.
• Q10. There are some tweet_ids with the same jpg_url.
• Missing values from images dataset (there are 2075 records in this DataFrame compared to the 2356 records in the df_twitter archive DataFrame)

DataFrame: df_twitter_counts

• There are some missing values (there are 2354 records in this DataFrame compared to the 2356 records in the df_twitter archive DataFrame)

Tidiness Issues

DataFrame: df_twitter

• T1. The Dog Stage variable is splitted into 4 different columns (doggo, floofer, pupper, puppo). Therefore, it is necessary to melt all the four stages of dogs into only one column.

DataFrame: df_img_pred

• T2. The dog breed prediction and the prediction confidence columns in the Image Predictions DataFrame (df_img_pred) should be packed into two unique different columns: breed_pred and confidence_pred.

DataFrame: df_twitter_counts

• T3. It is necesary to merge 'df_twitter_counts' into 'df_twitter'.

CLEANING DATA

Create copies of the originals DaraFrames:

# Create a copy of the DataFrame (df)
twitter_clean = df_twitter.copy()
# Create a copy of twitter_counts_df
twitter_counts_clean = df_twitter_counts.copy()
# Create a copy of img_pred.
img_pred_clean = df_img_pred.copy()

Q1. Text column contains truncated text instead of displayable text.

Define

In order to be able to analyze the text to get names, breed and rating information, I need to display the full text. I will do it by applying the code line that can be found in the code cell below back where I gathered the Twitter Archive.

Code

# This will set the no truncate for pandas
# pd.set_option('display.max_colwidth', -1)

Test

twitter_clean.text

0       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU                                                         
1       This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV    
2       This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB                     
3       This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ                                                               
4       This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f    
5       Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh    
6       Meet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\n\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxl
7       When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq                        
8       This is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0b                    
9       This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A         
10      This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme            
11      This is Bruno. He is a service shark. Only gets out of the water to assist you. 13/10 terrifyingly good boy https://t.co/u1XPQMl29g                                   
12      Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm                                    
13      This is Ted. He does his best. Sometimes that's not enough. But it's ok. 12/10 would assist https://t.co/f8dEDcrKSR                                                   
14      This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq                        
15      This is Oliver. You're witnessing one of his many brutal attacks. Seems to be playing with his victim. 13/10 fr*ckin frightening #BarkWeek https://t.co/WpHvrQedPb    
16      This is Jim. He found a fren. Taught him how to sit like the good boys. 12/10 for both https://t.co/chxruIOUJN                                                        
17      This is Zeke. He has a new stick. Very proud of it. Would like you to throw it for him without taking it. 13/10 would do my best https://t.co/HTQ77yNQ5K              
18      This is Ralphus. He's powering up. Attempting maximum borkdrive. 13/10 inspirational af https://t.co/YnYAFCTTiK                                                       
19      RT @dog_rates: This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX                            
20      This is Gerald. He was just told he didn't get the job he interviewed for. A h*ckin injustice. 12/10 didn't want the job anyway https://t.co/DK7iDPfuRX               
21      This is Jeffrey. He has a monopoly on the pool noodles. Currently running a 'boop for two' midweek sale. 13/10 h*ckin strategic https://t.co/PhrUk20Q64               
22      I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba                                  
23      This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX                                           
24      You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV                                                      
25      This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp             
26      This is Maya. She's very shy. Rarely leaves her cup. 13/10 would find her an environment to thrive in https://t.co/I6oNy0CgiT                                         
27      This is Mingus. He's a wonderful father to his smol pup. Confirmed 13/10, but he needs your help\n\nhttps://t.co/bVi0Yr4Cff https://t.co/ISvKOSkd5b                   
28      This is Derek. He's late for a dog meeting. 13/10 pet...al to the metal https://t.co/BCoWue0abA                                                                       
29      This is Roscoe. Another pupper fallen victim to spontaneous tongue ejections. Get the BlepiPen immediate. 12/10 deep breaths Roscoe https://t.co/RGE08MIJox           
                                                                                   ...                                                                                        
2326    This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t                          
2327    This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h                           
2328    Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE                             
2329    Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl                                                                                     
2330    Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM                          
2331    Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO                               
2332    Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB                                                      
2333    This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL                           
2334    This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0                            
2335    This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                              
2336    Can take selfies 11/10 https://t.co/ws2AMaNwPW                                                                                                                        
2337    Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk                                                                                    
2338    Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX                                
2339    Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq                                      
2340    Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C                          
2341    This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP                                  
2342    Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7                                         
2343    Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY                             
2344    Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx                             
2345    This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK                                                           
2346    Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p                               
2347    My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O                                          
2348    Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt                          
2349    This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc                            
2350    This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe                          
2351    Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq                                              
2352    This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx                             
2353    Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR                                    
2354    This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI                           
2355    Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj                                   
Name: text, Length: 2356, dtype: object

Q2. The data contains retweets and replies and I only want to investigate original tweet's ratings.

Define

Delete retweets and replies in df_twitter table filtering the NaN of retweeted_status_user_id in order to keep only the original tweets.

Code

# Delete retweets
twitter_clean= twitter_clean[twitter_clean.retweeted_status_id.isnull()];

# Delete replies
twitter_clean= twitter_clean[twitter_clean.in_reply_to_status_id.isnull()];

Test

# Check there are no retweets and replies
sum(~ ((twitter_clean.retweeted_status_id.isnull())&(twitter_clean.in_reply_to_status_id.isnull())))

0

Q.3 There are some columns in this dataframe that I won't be using for my analisys.

Define

I am droping the columns that I will not be using in my analysis from df_twitter dataframe: 'in_reply_to_status_id', 'in_reply_to_user_id', 'source', 'retweeted_status_id','retweeted_status_user_id', 'retweeted_status_timestamp', and 'expanded_urls'

Code

# Drop columns I do not need:
twitter_clean = twitter_clean.drop(['in_reply_to_status_id','in_reply_to_user_id','source',
                              'retweeted_status_id','retweeted_status_user_id',
                              'retweeted_status_timestamp'], axis=1)

Test

# Check the columns have been deleted
twitter_clean.columns

Index(['tweet_id', 'timestamp', 'text', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],
      dtype='object')

Q4. There are some invalid names such as 'a', 'actually', 'all', 'an', 'by', etc.

Define

To correct invalid names (having filtered by names starting with lowercase), in some tweet's, the real name appears after phrases such as: 'named', 'That is' and 'Name is'. I am going to use the if .. elif .. else statement to try extracting the dog's names from the text column and in case it is not possible, return NaN.

Code

# Create a new column 'text_split'
twitter_clean['text_split_words'] = twitter_clean['text'].str.split() #Split a string into a list where each word is a list item

corrected_names = []
# Write lambda function
def extract_names(case):
    # 'named'           
    if 'named' in case['text'] and re.match(r'[A-Z].*', case['text_split_words'][(case['text_split_words'].index('named') + 1)]): 
        corrected_names.append(case['text_split_words'][(case['text_split_words'].index('named') + 1)]) 
        # [A-Z].* --> Match any uppercase ASCII letter
    
    # 'That is'
    elif case['text'].startswith('That is ') and re.match(r'[A-Z].*', case['text_split_words'][2]):
        corrected_names.append(case['text_split_words'][2].strip('.').strip(','))
    
    # 'name is'
    elif case['text'].startswith('name is ') and re.match(r'[A-Z].*', case['text_split_words'][2]):
        corrected_names.append(case['text_split_words'][2].strip('.').strip(','))
              
    # 'Here we have'
    elif case['text'].startswith('Here we have ') and re.match(r'[A-Z].*', case['text_split_words'][3]):
        corrected_names.append(case['text_split_words'][3].strip('.').strip(','))
    
    # 'Say hello to'
    elif case['text'].startswith('Say hello to ') and re.match(r'[A-Z].*', case['text_split_words'][3]):
        corrected_names.append(case['text_split_words'][3].strip('.').strip(','))
     
    # 'Meet'
    elif case['text'].startswith('Meet ') and re.match(r'[A-Z].*', case['text_split_words'][1]):
        corrected_names.append(case['text_split_words'][1].strip('.').strip(','))
    
    # 'This is'
    elif case['text'].startswith('This is ') and re.match(r'[A-Z].*', case['text_split_words'][2]):
        corrected_names.append(case['text_split_words'][2].strip('.').strip(','))              
            
    else:
        corrected_names.append('None')
        
# Source https://github.com/sanjeevai/Wrangle_and_Analyze_data/blob/master/wrangle_act.ipynb

# apply the written function 
twitter_clean.apply(extract_names, axis=1)
twitter_clean['corrected_names'] = corrected_names

# Eliminate the drop at the end of the names
twitter_clean.corrected_names.replace('.', '',inplace=True)

# delete the old name column and the tex split column
twitter_clean.drop(['name'], axis=1, inplace=True)
twitter_clean.drop(['text_split_words'], axis=1, inplace=True)

Test

# Check if there are any names starting with lowercase
len(twitter_clean.loc[(twitter_clean['corrected_names'].str.islower())])

0

# Check if the number of 'None' names has deceased (before cleaning: None   745)
twitter_clean.corrected_names.value_counts()

None        687
Lucy        11 
Charlie     11 
Oliver      10 
Cooper      10 
Tucker      9  
Penny       9  
Lola        8  
Winston     8  
Sadie       8  
Daisy       7  
Stanley     6  
Koda        6  
Oscar       6  
Bo          6  
Bella       6  
Bailey      6  
Toby        6  
Jax         6  
Chester     5  
Louis       5  
Milo        5  
Bentley     5  
Buddy       5  
Scout       5  
Leo         5  
Rusty       5  
Chip        4  
Dave        4  
Finn        4  
           ..  
Kona        1  
Berta.      1  
Evy         1  
Lillie      1  
Alfredo.    1  
Brian       1  
Boots       1  
Atlas       1  
Odin        1  
Lucky       1  
Beckham     1  
Noah        1  
Schnozz     1  
Brandy      1  
Walker      1  
Huck        1  
Klein       1  
Brooks      1  
Brady       1  
Tuck        1  
Alphred.    1  
Iggy        1  
Carper      1  
Chuk.       1  
Liam        1  
Bode        1  
Dallas      1  
Zooey       1  
Blanket     1  
Livvie      1  
Name: corrected_names, Length: 956, dtype: int64

Q5. Erroneous datatypes: tweet_id, timestamp.

Define

Convert tweet_id to string and timestamp to datetime data type. (I found more datatype errors but they have been corrected in other cleaning steps)

Code

# Change tweet_id type to str from tables and df_twitter df_img_pred
twitter_clean.tweet_id = twitter_clean.tweet_id.astype(str)
img_pred_clean.tweet_id = img_pred_clean.tweet_id.astype(str)

# Change timestamp type to datetime
twitter_clean.timestamp = pd.to_datetime(df_twitter.timestamp)

Test

twitter_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2097 entries, 0 to 2355
Data columns (total 11 columns):
tweet_id              2097 non-null object
timestamp             2097 non-null datetime64[ns]
text                  2097 non-null object
expanded_urls         2094 non-null object
rating_numerator      2097 non-null int64
rating_denominator    2097 non-null int64
doggo                 2097 non-null object
floofer               2097 non-null object
pupper                2097 non-null object
puppo                 2097 non-null object
corrected_names       2097 non-null object
dtypes: datetime64[ns](1), int64(2), object(8)
memory usage: 196.6+ KB

img_pred_clean.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
tweet_id    2075 non-null object
jpg_url     2075 non-null object
img_num     2075 non-null int64
p1          2075 non-null object
p1_conf     2075 non-null float64
p1_dog      2075 non-null bool
p2          2075 non-null object
p2_conf     2075 non-null float64
p2_dog      2075 non-null bool
p3          2075 non-null object
p3_conf     2075 non-null float64
p3_dog      2075 non-null bool
dtypes: bool(3), float64(3), int64(1), object(5)
memory usage: 152.1+ KB

Q6 Some records have more than one dog stage, due to detection errors and the fact that some tweets rate 2 dogs.

Define

While assesing I detected tweets that were rating two dogs, I am going to delete those as I only want to investigate tweets rating one dog. I am also going to correct the dog stages for those tweets with two stages but only rating one dog.

Code

# Delete tweets rating two dogs
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="808106460588765185"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="802265048156610565"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="781308096455073793"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="775898661951791106"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="770093767776997377"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="759793422261743616"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="741067306818797568"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="733109485275860992"].index, inplace=True)

# Correct dog stages
twitter_clean.loc[twitter_clean['tweet_id']=="854010172552949760",'doggo'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="855851453814013952",'doggo'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="817777686764523521",'doggo'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="801115127852503040",'pupper'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="785639753186217984",'pupper'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="751583847268179968",'pupper'] = ''

Test

twitter_clean[((twitter_clean.doggo != "None") & (twitter_clean.floofer != "None"))&
              ((twitter_clean.doggo != '') & (twitter_clean.floofer != ''))]

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	doggo	floofer	pupper	puppo	corrected_names

twitter_clean[((twitter_clean.doggo != "None") & (twitter_clean.pupper != "None"))&
              ((twitter_clean.doggo != '') & (twitter_clean.pupper != ''))]

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	doggo	floofer	pupper	puppo	corrected_names

twitter_clean[((twitter_clean.doggo != "None") & (twitter_clean.puppo != "None"))&
              ((twitter_clean.doggo != '') & (twitter_clean.puppo != ''))]

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	doggo	floofer	pupper	puppo	corrected_names

Q7. For several columns, nulls are marked as None.

Define

The missings for name column in archive table (twitter_clean) are represented as "None" instead of Null, therefore I will replace this missing data from None to NaN using .replace function. The missings for doggo, floofer, pupper, and puppo columns in archive table are represented as "None". I will replace those "None" for '' to be able to concatenate the four columns (in further cleaning steps) getting one with the dog's stage.

Code

# Replace 'None' values for NaN
twitter_clean['corrected_names'].replace('None', np.nan, inplace=True)
twitter_clean['doggo'].replace('None', '', inplace=True)
twitter_clean['floofer'].replace('None', '', inplace=True)
twitter_clean['pupper'].replace('None', '', inplace=True)
twitter_clean['puppo'].replace('None', '', inplace=True)

Test

# Check there are not 'None' values in the columns
print("'None' values in name column:", twitter_clean['corrected_names'].str.contains('None').sum())
print("'None' values in doggo column:", twitter_clean['doggo'].str.contains('None').sum())
print("'None' values in floofer column:", twitter_clean['floofer'].str.contains('None').sum())
print("'None' values in pupper column:", twitter_clean['pupper'].str.contains('None').sum())
print("'None' values in puppo column:", twitter_clean['puppo'].str.contains('None').sum())

'None' values in name column: 0
'None' values in doggo column: 0
'None' values in floofer column: 0
'None' values in pupper column: 0
'None' values in puppo column: 0

T1. In the Twitter Archive DataFrame (twitter_clean) the Dog Stage variable is splitted into 4 different columns (doggo, floofer, pupper, puppo). Therefore, it is necessary to melt all the four stages of dogs into only one column.

Define

Tansform the 4 columns into one by concatenating them and obtaining the dog_stage column with the stege of each dog.

Code

twitter_clean.groupby(["doggo", "floofer", "pupper", "puppo"]).size().reset_index().rename(columns={0: "count"})

	doggo	floofer	pupper	puppo	count
0					1761
1				puppo	24
2			pupper		222
3		floofer			10
4	doggo				75

# Concatenate the dog stage columns into one single column (dog_stage)
twitter_clean['dog_stage'] = twitter_clean.doggo + twitter_clean.floofer + twitter_clean.pupper + twitter_clean.puppo
# Set the empty dog_stage values as NaN
twitter_clean.loc[twitter_clean.dog_stage == '', 'dog_stage'] = np.nan

# Delete the useless columns ("doggo", "floofer", "pupper", "puppo")
twitter_clean.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis=1, inplace=True)

Test

twitter_clean

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage
0	892420643555336193	2017-08-01 16:23:56	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	https://twitter.com/dog_rates/status/892420643555336193/photo/1	13	10	Phineas	NaN
1	892177421306343426	2017-08-01 00:17:27	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	https://twitter.com/dog_rates/status/892177421306343426/photo/1	13	10	Tilly	NaN
2	891815181378084864	2017-07-31 00:18:03	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	https://twitter.com/dog_rates/status/891815181378084864/photo/1	12	10	Archie	NaN
3	891689557279858688	2017-07-30 15:58:51	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	https://twitter.com/dog_rates/status/891689557279858688/photo/1	13	10	Darla	NaN
4	891327558926688256	2017-07-29 16:00:24	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1	12	10	Franklin	NaN
5	891087950875897856	2017-07-29 00:08:17	Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh	https://twitter.com/dog_rates/status/891087950875897856/photo/1	13	10	NaN	NaN
6	890971913173991426	2017-07-28 16:27:12	Meet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\n\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxl	https://gofundme.com/ydvmve-surgery-for-jax,https://twitter.com/dog_rates/status/890971913173991426/photo/1	13	10	Jax	NaN
7	890729181411237888	2017-07-28 00:22:40	When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq	https://twitter.com/dog_rates/status/890729181411237888/photo/1,https://twitter.com/dog_rates/status/890729181411237888/photo/1	13	10	NaN	NaN
8	890609185150312448	2017-07-27 16:25:51	This is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0b	https://twitter.com/dog_rates/status/890609185150312448/photo/1	13	10	Zoey	NaN
9	890240255349198849	2017-07-26 15:59:51	This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A	https://twitter.com/dog_rates/status/890240255349198849/photo/1	14	10	Cassie	doggo
10	890006608113172480	2017-07-26 00:31:25	This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme	https://twitter.com/dog_rates/status/890006608113172480/photo/1,https://twitter.com/dog_rates/status/890006608113172480/photo/1	13	10	Koda	NaN
11	889880896479866881	2017-07-25 16:11:53	This is Bruno. He is a service shark. Only gets out of the water to assist you. 13/10 terrifyingly good boy https://t.co/u1XPQMl29g	https://twitter.com/dog_rates/status/889880896479866881/photo/1	13	10	Bruno	NaN
12	889665388333682689	2017-07-25 01:55:32	Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm	https://twitter.com/dog_rates/status/889665388333682689/photo/1	13	10	NaN	puppo
13	889638837579907072	2017-07-25 00:10:02	This is Ted. He does his best. Sometimes that's not enough. But it's ok. 12/10 would assist https://t.co/f8dEDcrKSR	https://twitter.com/dog_rates/status/889638837579907072/photo/1,https://twitter.com/dog_rates/status/889638837579907072/photo/1	12	10	Ted	NaN
14	889531135344209921	2017-07-24 17:02:04	This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq	https://twitter.com/dog_rates/status/889531135344209921/photo/1	13	10	Stuart	puppo
15	889278841981685760	2017-07-24 00:19:32	This is Oliver. You're witnessing one of his many brutal attacks. Seems to be playing with his victim. 13/10 fr*ckin frightening #BarkWeek https://t.co/WpHvrQedPb	https://twitter.com/dog_rates/status/889278841981685760/video/1	13	10	Oliver	NaN
16	888917238123831296	2017-07-23 00:22:39	This is Jim. He found a fren. Taught him how to sit like the good boys. 12/10 for both https://t.co/chxruIOUJN	https://twitter.com/dog_rates/status/888917238123831296/photo/1	12	10	Jim	NaN
17	888804989199671297	2017-07-22 16:56:37	This is Zeke. He has a new stick. Very proud of it. Would like you to throw it for him without taking it. 13/10 would do my best https://t.co/HTQ77yNQ5K	https://twitter.com/dog_rates/status/888804989199671297/photo/1,https://twitter.com/dog_rates/status/888804989199671297/photo/1	13	10	Zeke	NaN
18	888554962724278272	2017-07-22 00:23:06	This is Ralphus. He's powering up. Attempting maximum borkdrive. 13/10 inspirational af https://t.co/YnYAFCTTiK	https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1	13	10	Ralphus	NaN
20	888078434458587136	2017-07-20 16:49:33	This is Gerald. He was just told he didn't get the job he interviewed for. A h*ckin injustice. 12/10 didn't want the job anyway https://t.co/DK7iDPfuRX	https://twitter.com/dog_rates/status/888078434458587136/photo/1,https://twitter.com/dog_rates/status/888078434458587136/photo/1	12	10	Gerald	NaN
21	887705289381826560	2017-07-19 16:06:48	This is Jeffrey. He has a monopoly on the pool noodles. Currently running a 'boop for two' midweek sale. 13/10 h*ckin strategic https://t.co/PhrUk20Q64	https://twitter.com/dog_rates/status/887705289381826560/photo/1	13	10	Jeffrey	NaN
22	887517139158093824	2017-07-19 03:39:09	I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba	https://twitter.com/dog_rates/status/887517139158093824/video/1	14	10	NaN	NaN
23	887473957103951883	2017-07-19 00:47:34	This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX	https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1	13	10	Canela	NaN
24	887343217045368832	2017-07-18 16:08:03	You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV	https://twitter.com/dog_rates/status/887343217045368832/video/1	13	10	NaN	NaN
25	887101392804085760	2017-07-18 00:07:08	This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp	https://twitter.com/dog_rates/status/887101392804085760/photo/1	12	10	NaN	NaN
26	886983233522544640	2017-07-17 16:17:36	This is Maya. She's very shy. Rarely leaves her cup. 13/10 would find her an environment to thrive in https://t.co/I6oNy0CgiT	https://twitter.com/dog_rates/status/886983233522544640/photo/1,https://twitter.com/dog_rates/status/886983233522544640/photo/1	13	10	Maya	NaN
27	886736880519319552	2017-07-16 23:58:41	This is Mingus. He's a wonderful father to his smol pup. Confirmed 13/10, but he needs your help\n\nhttps://t.co/bVi0Yr4Cff https://t.co/ISvKOSkd5b	https://www.gofundme.com/mingusneedsus,https://twitter.com/dog_rates/status/886736880519319552/photo/1,https://twitter.com/dog_rates/status/886736880519319552/photo/1	13	10	Mingus	NaN
28	886680336477933568	2017-07-16 20:14:00	This is Derek. He's late for a dog meeting. 13/10 pet...al to the metal https://t.co/BCoWue0abA	https://twitter.com/dog_rates/status/886680336477933568/photo/1	13	10	Derek	NaN
29	886366144734445568	2017-07-15 23:25:31	This is Roscoe. Another pupper fallen victim to spontaneous tongue ejections. Get the BlepiPen immediate. 12/10 deep breaths Roscoe https://t.co/RGE08MIJox	https://twitter.com/dog_rates/status/886366144734445568/photo/1,https://twitter.com/dog_rates/status/886366144734445568/photo/1	12	10	Roscoe	pupper
31	886258384151887873	2017-07-15 16:17:19	This is Waffles. His doggles are pupside down. Unsure how to fix. 13/10 someone assist Waffles https://t.co/xZDA9Qsq1O	https://twitter.com/dog_rates/status/886258384151887873/photo/1	13	10	Waffles	NaN
...	...	...	...	...	...	...	...	...
2326	666411507551481857	2015-11-17 00:24:19	This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t	https://twitter.com/dog_rates/status/666411507551481857/photo/1	2	10	NaN	NaN
2327	666407126856765440	2015-11-17 00:06:54	This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h	https://twitter.com/dog_rates/status/666407126856765440/photo/1	7	10	NaN	NaN
2328	666396247373291520	2015-11-16 23:23:41	Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE	https://twitter.com/dog_rates/status/666396247373291520/photo/1	9	10	NaN	NaN
2329	666373753744588802	2015-11-16 21:54:18	Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl	https://twitter.com/dog_rates/status/666373753744588802/photo/1	11	10	NaN	NaN
2330	666362758909284353	2015-11-16 21:10:36	Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM	https://twitter.com/dog_rates/status/666362758909284353/photo/1	6	10	NaN	NaN
2331	666353288456101888	2015-11-16 20:32:58	Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO	https://twitter.com/dog_rates/status/666353288456101888/photo/1	8	10	NaN	NaN
2332	666345417576210432	2015-11-16 20:01:42	Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB	https://twitter.com/dog_rates/status/666345417576210432/photo/1	10	10	NaN	NaN
2333	666337882303524864	2015-11-16 19:31:45	This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL	https://twitter.com/dog_rates/status/666337882303524864/photo/1	9	10	NaN	NaN
2334	666293911632134144	2015-11-16 16:37:02	This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0	https://twitter.com/dog_rates/status/666293911632134144/photo/1	3	10	NaN	NaN
2335	666287406224695296	2015-11-16 16:11:11	This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv	https://twitter.com/dog_rates/status/666287406224695296/photo/1	1	2	NaN	NaN
2336	666273097616637952	2015-11-16 15:14:19	Can take selfies 11/10 https://t.co/ws2AMaNwPW	https://twitter.com/dog_rates/status/666273097616637952/photo/1	11	10	NaN	NaN
2337	666268910803644416	2015-11-16 14:57:41	Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk	https://twitter.com/dog_rates/status/666268910803644416/photo/1	10	10	NaN	NaN
2338	666104133288665088	2015-11-16 04:02:55	Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX	https://twitter.com/dog_rates/status/666104133288665088/photo/1	1	10	NaN	NaN
2339	666102155909144576	2015-11-16 03:55:04	Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq	https://twitter.com/dog_rates/status/666102155909144576/photo/1	11	10	NaN	NaN
2340	666099513787052032	2015-11-16 03:44:34	Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C	https://twitter.com/dog_rates/status/666099513787052032/photo/1	8	10	NaN	NaN
2341	666094000022159362	2015-11-16 03:22:39	This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP	https://twitter.com/dog_rates/status/666094000022159362/photo/1	9	10	NaN	NaN
2342	666082916733198337	2015-11-16 02:38:37	Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7	https://twitter.com/dog_rates/status/666082916733198337/photo/1	6	10	NaN	NaN
2343	666073100786774016	2015-11-16 01:59:36	Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY	https://twitter.com/dog_rates/status/666073100786774016/photo/1	10	10	NaN	NaN
2344	666071193221509120	2015-11-16 01:52:02	Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx	https://twitter.com/dog_rates/status/666071193221509120/photo/1	9	10	NaN	NaN
2345	666063827256086533	2015-11-16 01:22:45	This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK	https://twitter.com/dog_rates/status/666063827256086533/photo/1	10	10	NaN	NaN
2346	666058600524156928	2015-11-16 01:01:59	Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p	https://twitter.com/dog_rates/status/666058600524156928/photo/1	8	10	NaN	NaN
2347	666057090499244032	2015-11-16 00:55:59	My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O	https://twitter.com/dog_rates/status/666057090499244032/photo/1	9	10	NaN	NaN
2348	666055525042405380	2015-11-16 00:49:46	Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt	https://twitter.com/dog_rates/status/666055525042405380/photo/1	10	10	NaN	NaN
2349	666051853826850816	2015-11-16 00:35:11	This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc	https://twitter.com/dog_rates/status/666051853826850816/photo/1	2	10	NaN	NaN
2350	666050758794694657	2015-11-16 00:30:50	This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe	https://twitter.com/dog_rates/status/666050758794694657/photo/1	10	10	NaN	NaN
2351	666049248165822465	2015-11-16 00:24:50	Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq	https://twitter.com/dog_rates/status/666049248165822465/photo/1	5	10	NaN	NaN
2352	666044226329800704	2015-11-16 00:04:52	This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx	https://twitter.com/dog_rates/status/666044226329800704/photo/1	6	10	NaN	NaN
2353	666033412701032449	2015-11-15 23:21:54	Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR	https://twitter.com/dog_rates/status/666033412701032449/photo/1	9	10	NaN	NaN
2354	666029285002620928	2015-11-15 23:05:30	This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI	https://twitter.com/dog_rates/status/666029285002620928/photo/1	7	10	NaN	NaN
2355	666020888022790149	2015-11-15 22:32:08	Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj	https://twitter.com/dog_rates/status/666020888022790149/photo/1	8	10	NaN	NaN

2092 rows × 8 columns

twitter_clean.dog_stage.value_counts()

pupper     222
doggo      75 
puppo      24 
floofer    10 
Name: dog_stage, dtype: int64

Q8. There are several tweets with a denominator different to 10, might be related to more than just 1 dog per tweet.

Define

twitter_clean[twitter_clean['rating_denominator']!=10]

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage
433	820690176645140481	2017-01-15 17:52:40	The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd	https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1	84	70	NaN	NaN
516	810984652412424192	2016-12-19 23:06:23	Meet Sam. She smiles 24/7 & secretly aspires to be a reindeer. \nKeep Sam smiling by clicking and sharing this link:\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx	https://www.gofundme.com/sams-smile,https://twitter.com/dog_rates/status/810984652412424192/photo/1	24	7	Sam	NaN
902	758467244762497024	2016-07-28 01:00:57	Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE	https://twitter.com/dog_rates/status/758467244762497024/video/1	165	150	NaN	NaN
1068	740373189193256964	2016-06-08 02:41:38	After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ	https://twitter.com/dog_rates/status/740373189193256964/photo/1,https://twitter.com/dog_rates/status/740373189193256964/photo/1,https://twitter.com/dog_rates/status/740373189193256964/photo/1,https://twitter.com/dog_rates/status/740373189193256964/photo/1	9	11	NaN	NaN
1120	731156023742988288	2016-05-13 16:15:54	Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv	https://twitter.com/dog_rates/status/731156023742988288/photo/1	204	170	NaN	NaN
1165	722974582966214656	2016-04-21 02:25:47	Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a	https://twitter.com/dog_rates/status/722974582966214656/photo/1	4	20	NaN	NaN
1202	716439118184652801	2016-04-03 01:36:11	This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq	https://twitter.com/dog_rates/status/716439118184652801/photo/1	50	50	Bluebert	NaN
1228	713900603437621249	2016-03-27 01:29:02	Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1	https://twitter.com/dog_rates/status/713900603437621249/photo/1	99	90	NaN	NaN
1254	710658690886586372	2016-03-18 02:46:49	Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12	https://twitter.com/dog_rates/status/710658690886586372/photo/1	80	80	NaN	NaN
1274	709198395643068416	2016-03-14 02:04:08	From left to right:\nCletus, Jerome, Alejandro, Burp, & Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK	https://twitter.com/dog_rates/status/709198395643068416/photo/1	45	50	NaN	NaN
1351	704054845121142784	2016-02-28 21:25:30	Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa	https://twitter.com/dog_rates/status/704054845121142784/photo/1	60	50	NaN	NaN
1433	697463031882764288	2016-02-10 16:51:59	Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ	https://twitter.com/dog_rates/status/697463031882764288/photo/1	44	40	NaN	NaN
1635	684222868335505415	2016-01-05 04:00:18	Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55	https://twitter.com/dog_rates/status/684222868335505415/photo/1	121	110	NaN	NaN
1662	682962037429899265	2016-01-01 16:30:13	This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5	https://twitter.com/dog_rates/status/682962037429899265/photo/1	7	11	Darrel	NaN
1779	677716515794329600	2015-12-18 05:06:23	IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq	https://twitter.com/dog_rates/status/677716515794329600/photo/1	144	120	NaN	NaN
1843	675853064436391936	2015-12-13 01:41:41	Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw	https://twitter.com/dog_rates/status/675853064436391936/photo/1,https://twitter.com/dog_rates/status/675853064436391936/photo/1	88	80	NaN	NaN
2335	666287406224695296	2015-11-16 16:11:11	This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv	https://twitter.com/dog_rates/status/666287406224695296/photo/1	1	2	NaN	NaN

By visually inspecting tweets with a rating denominator other than the standard metric (10), I can determine, using the text and the urls, the actual denominator (as well as the numerator).

As mentioned above, I just want to analyze tweets that rate one dog, therefore I will remove rows for which the tweet rates more than one dog.

tweet_id	Corrected rating_denominator
758467244762497024	several dogs--> Drop row
820690176645140481	More than 1 dog--> Drop row
810984652412424192	NaN --> Drop row
740373189193256964	10
731156023742988288	2 dogs --> Drop row
722974582966214656	2 dogs --> Drop row
716439118184652801	10
713900603437621249	several dogs--> Drop row
710658690886586372	several dogs--> Drop row
709198395643068416	several dogs--> Drop row
704054845121142784	several dogs--> Drop row
697463031882764288	several dogs--> Drop row
684222868335505415	several dogs--> Drop row
682962037429899265	10
677716515794329600	several dogs--> Drop row
675853064436391936	several dogs--> Drop row
666287406224695296	10

tweet_id	Corrected rating_numerator
740373189193256964	14
716439118184652801	11
682962037429899265	10
666287406224695296	9

Code

# Delete tweets rating two dogs
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="820690176645140481"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="758467244762497024"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="810984652412424192"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="731156023742988288"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="722974582966214656"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="713900603437621249"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="710658690886586372"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="709198395643068416"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="704054845121142784"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="697463031882764288"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="684222868335505415"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="677716515794329600"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="675853064436391936"].index, inplace=True)

# Correct denominators
twitter_clean.loc[twitter_clean['tweet_id']=="740373189193256964",'rating_denominator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="716439118184652801",'rating_denominator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="682962037429899265",'rating_denominator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="666287406224695296",'rating_denominator'] = 10.0

# Correct numerators
twitter_clean.loc[twitter_clean['tweet_id']=="740373189193256964",'rating_numerator'] = 14.0
twitter_clean.loc[twitter_clean['tweet_id']=="716439118184652801",'rating_numerator'] = 11.0
twitter_clean.loc[twitter_clean['tweet_id']=="682962037429899265",'rating_numerator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="666287406224695296",'rating_numerator'] = 9.0

Test

twitter_clean[twitter_clean['rating_denominator']!=10]

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage

twitter_clean.rating_denominator.value_counts()

10.0    2079
Name: rating_denominator, dtype: int64

Q9. There are several tweets with really high values for the numerators (e.g.: from 100 to 1776) and numerators with decimals.

Define

In WeRateDogs it is accepted that the numerators can be greater than 10, however I will drop those numerators greater than 20 since they do not represent the norm and can be considered as spikes that would skew the sample. I will also correct the values misinterpreted by the algorithm as they are a decimal number.

Code

# Display the numerator values higher than 20
twitter_clean[twitter_clean['rating_numerator']>20.0]

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage
695	786709082849828864	2016-10-13 23:23:56	This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS	https://twitter.com/dog_rates/status/786709082849828864/photo/1	75.0	10.0	Logan	NaN
763	778027034220126208	2016-09-20 00:24:34	This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq	https://twitter.com/dog_rates/status/778027034220126208/photo/1	27.0	10.0	Sophie	pupper
979	749981277374128128	2016-07-04 15:00:45	This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh	https://twitter.com/dog_rates/status/749981277374128128/photo/1	1776.0	10.0	Atticus	NaN
1712	680494726643068929	2015-12-25 21:06:00	Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD	https://twitter.com/dog_rates/status/680494726643068929/photo/1	26.0	10.0	NaN	NaN
2074	670842764863651840	2015-11-29 05:52:33	After so many requests... here you go.\n\nGood dogg. 420/10 https://t.co/yfAAo1gdeY	https://twitter.com/dog_rates/status/670842764863651840/photo/1	420.0	10.0	NaN	NaN

# Drop numerators greater than 20
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="749981277374128128"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="670842764863651840"].index, inplace=True)

# View tweets with decimals in rating in 'text' column
twitter_clean[twitter_clean.text.str.contains(r"(\d+\.\d*\/\d+)")]

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
  

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage
45	883482846933004288	2017-07-08 00:28:19	This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948	https://twitter.com/dog_rates/status/883482846933004288/photo/1,https://twitter.com/dog_rates/status/883482846933004288/photo/1	5.0	10.0	Bella	NaN
695	786709082849828864	2016-10-13 23:23:56	This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS	https://twitter.com/dog_rates/status/786709082849828864/photo/1	75.0	10.0	Logan	NaN
763	778027034220126208	2016-09-20 00:24:34	This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq	https://twitter.com/dog_rates/status/778027034220126208/photo/1	27.0	10.0	Sophie	pupper
1712	680494726643068929	2015-12-25 21:06:00	Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD	https://twitter.com/dog_rates/status/680494726643068929/photo/1	26.0	10.0	NaN	NaN

# Correct numerators with the comments in the text column
twitter_clean.loc[twitter_clean['tweet_id'] == "883482846933004288",'rating_numerator'] = 13.5
twitter_clean.loc[twitter_clean['tweet_id'] == "786709082849828864",'rating_numerator'] = 9.75
twitter_clean.loc[twitter_clean['tweet_id'] == "778027034220126208",'rating_numerator'] = 11.27
twitter_clean.loc[twitter_clean['tweet_id'] == "680494726643068929",'rating_numerator'] = 11.26

Test

# Check there are not numerators greater than 20
twitter_clean[twitter_clean['rating_numerator']>20.0]

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage

# check that numerators with decimals are corrected and match the rating in the text
twitter_clean[twitter_clean.text.str.contains(r"(\d+\.\d*\/\d+)")].loc[:,['text','rating_numerator','rating_denominator']]

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
  

	text	rating_numerator	rating_denominator
45	This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948	13.50	10.0
695	This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS	9.75	10.0
763	This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq	11.27	10.0
1712	Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD	11.26	10.0

Q10. There are some tweet_ids with the same jpg_url.

Define

Delete duplicated urls from img_pred_clean

Code

# Drop duplicated urls
img_pred_clean.drop_duplicates(['jpg_url'],keep='first',inplace=True)

Test

sum(img_pred_clean['jpg_url'].duplicated())

0

T2. The dog breed prediction and the prediction confidence columns in the Image Predictions DataFrame (df_img_pred) should be packed into two unique different columns: breed_pred and confidence_pred.

Define

Create a new DataFrame that contains each stub name as a variable, with new index (i, j) using the pandas.wide_to_long function. Each row of these wide variables are assumed to be uniquely identified by i ('tweet_id', 'jpg_url', 'img_num')

Source: pandas.wide_to_long

Code

# modify df_img_pred DataFrame column naming to be able to apply the pandas wide_to_long function 
img_pred_clean.rename(columns={"p1": "pred_1", "p1_conf": "conf_1", "p1_dog": "dog_1",
                  "p2": "pred_2", "p2_conf": "conf_2", "p2_dog": "dog_2",
                  "p3": "pred_3", "p3_conf": "conf_3", "p3_dog": "dog_3"},inplace=True)
# Columns conversion wide to long (Multiple column melt)
img_pred_clean=pd.wide_to_long(img_pred_clean, stubnames=['pred', 'conf', 'dog'],
                            i=['tweet_id', 'jpg_url', 'img_num'], j='prediction_number', sep='_')\
  .reset_index()

# Change prediction_number type to str
img_pred_clean.prediction_number = img_pred_clean.prediction_number.astype(str)

Test

# Visually check our new DataFrame layout
img_pred_clean

	tweet_id	jpg_url	img_num	prediction_number	pred	conf	dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	1	Welsh_springer_spaniel	0.465074	True
1	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	2	collie	0.156665	True
2	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	3	Shetland_sheepdog	0.061428	True
3	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	1	redbone	0.506826	True
4	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	2	miniature_pinscher	0.074192	True
5	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	3	Rhodesian_ridgeback	0.072010	True
6	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	1	German_shepherd	0.596461	True
7	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	2	malinois	0.138584	True
8	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	3	bloodhound	0.116197	True
9	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	1	Rhodesian_ridgeback	0.408143	True
10	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	2	redbone	0.360687	True
11	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	3	miniature_pinscher	0.222752	True
12	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	1	miniature_pinscher	0.560311	True
13	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	2	Rottweiler	0.243682	True
14	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	3	Doberman	0.154629	True
15	666050758794694657	https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg	1	1	Bernese_mountain_dog	0.651137	True
16	666050758794694657	https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg	1	2	English_springer	0.263788	True
17	666050758794694657	https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg	1	3	Greater_Swiss_Mountain_dog	0.016199	True
18	666051853826850816	https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg	1	1	box_turtle	0.933012	False
19	666051853826850816	https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg	1	2	mud_turtle	0.045885	False
20	666051853826850816	https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg	1	3	terrapin	0.017885	False
21	666055525042405380	https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg	1	1	chow	0.692517	True
22	666055525042405380	https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg	1	2	Tibetan_mastiff	0.058279	True
23	666055525042405380	https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg	1	3	fur_coat	0.054449	False
24	666057090499244032	https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg	1	1	shopping_cart	0.962465	False
25	666057090499244032	https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg	1	2	shopping_basket	0.014594	False
26	666057090499244032	https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg	1	3	golden_retriever	0.007959	True
27	666058600524156928	https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg	1	1	miniature_poodle	0.201493	True
28	666058600524156928	https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg	1	2	komondor	0.192305	True
29	666058600524156928	https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg	1	3	soft-coated_wheaten_terrier	0.082086	True
...	...	...	...	...	...	...	...
5997	890240255349198849	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	1	Pembroke	0.511319	True
5998	890240255349198849	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	2	Cardigan	0.451038	True
5999	890240255349198849	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	3	Chihuahua	0.029248	True
6000	890609185150312448	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	1	Irish_terrier	0.487574	True
6001	890609185150312448	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	2	Irish_setter	0.193054	True
6002	890609185150312448	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	3	Chesapeake_Bay_retriever	0.118184	True
6003	890729181411237888	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	1	Pomeranian	0.566142	True
6004	890729181411237888	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	2	Eskimo_dog	0.178406	True
6005	890729181411237888	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	3	Pembroke	0.076507	True
6006	890971913173991426	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	1	Appenzeller	0.341703	True
6007	890971913173991426	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	2	Border_collie	0.199287	True
6008	890971913173991426	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	3	ice_lolly	0.193548	False
6009	891087950875897856	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	1	Chesapeake_Bay_retriever	0.425595	True
6010	891087950875897856	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	2	Irish_terrier	0.116317	True
6011	891087950875897856	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	3	Indian_elephant	0.076902	False
6012	891327558926688256	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	1	basset	0.555712	True
6013	891327558926688256	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	2	English_springer	0.225770	True
6014	891327558926688256	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	3	German_short-haired_pointer	0.175219	True
6015	891689557279858688	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	1	paper_towel	0.170278	False
6016	891689557279858688	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	2	Labrador_retriever	0.168086	True
6017	891689557279858688	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	3	spatula	0.040836	False
6018	891815181378084864	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	1	Chihuahua	0.716012	True
6019	891815181378084864	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	2	malamute	0.078253	True
6020	891815181378084864	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	3	kelpie	0.031379	True
6021	892177421306343426	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	1	Chihuahua	0.323581	True
6022	892177421306343426	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	2	Pekinese	0.090647	True
6023	892177421306343426	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	3	papillon	0.068957	True
6024	892420643555336193	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	1	orange	0.097049	False
6025	892420643555336193	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	2	bagel	0.085851	False
6026	892420643555336193	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	3	banana	0.076110	False

6027 rows × 7 columns

T3. It is necesary to merge 'df_twitter_counts' into 'df_twitter'.

Define

Merge twitter_clean with twitter_counts_clean using only keys from twitter_clean with 'tweet_id' as the column level names to join on

Code

#convert both Key columns str
twitter_counts_clean['tweet_id'] = twitter_counts_clean['tweet_id'].astype(str)
twitter_clean['tweet_id'] = twitter_clean['tweet_id'].astype(str)

# Merge twitter_clean with twitter_counts_clean.
twitter_complete = pd.merge(twitter_clean,twitter_counts_clean,how = 'left',on = 'tweet_id')

Test

twitter_complete.head()

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage	retweets	favorites
0	892420643555336193	2017-08-01 16:23:56	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	https://twitter.com/dog_rates/status/892420643555336193/photo/1	13.0	10.0	Phineas	NaN	8853	39467
1	892177421306343426	2017-08-01 00:17:27	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	https://twitter.com/dog_rates/status/892177421306343426/photo/1	13.0	10.0	Tilly	NaN	6514	33819
2	891815181378084864	2017-07-31 00:18:03	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	https://twitter.com/dog_rates/status/891815181378084864/photo/1	12.0	10.0	Archie	NaN	4328	25461
3	891689557279858688	2017-07-30 15:58:51	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	https://twitter.com/dog_rates/status/891689557279858688/photo/1	13.0	10.0	Darla	NaN	8964	42908
4	891327558926688256	2017-07-29 16:00:24	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1	12.0	10.0	Franklin	NaN	9774	41048

STORE DATA

#Store the clean DataFrames in a CSV file
twitter_complete.to_csv('twitter_complete.csv', index=False, encoding = 'utf-8')
img_pred_clean.to_csv('img_pred_clean.csv', index=False, encoding = 'utf-8')

DATA ANALYSIS & CONCLUSIONS

# Read and Visualize twetter DataFrame
twitter_complete = pd.read_csv('twitter_complete.csv')
twitter_complete.head(3)

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage	retweets	favorites
0	892420643555336193	2017-08-01 16:23:56	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10.0	Phineas	NaN	8853	39467
1	892177421306343426	2017-08-01 00:17:27	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13.0	10.0	Tilly	NaN	6514	33819
2	891815181378084864	2017-07-31 00:18:03	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12.0	10.0	Archie	NaN	4328	25461

# Read and Visualize image prediction DataFrame
img_pred_clean = pd.read_csv('img_pred_clean.csv')
img_pred_clean.head()

	tweet_id	jpg_url	img_num	prediction_number	pred	conf	dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	1	Welsh_springer_spaniel	0.465074	True
1	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	2	collie	0.156665	True
2	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	3	Shetland_sheepdog	0.061428	True
3	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	1	redbone	0.506826	True
4	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	2	miniature_pinscher	0.074192	True

# Create a new rating column
twitter_complete['Rating']=twitter_complete.rating_numerator/twitter_complete.rating_denominator
twitter_complete.Rating.sort_values().value_counts()

1.200    481
1.000    437
1.100    414
1.300    287
0.900    153
0.800     98
0.700     51
1.400     39
0.500     33
0.600     32
0.300     19
0.400     15
0.200      9
0.100      4
1.127      1
1.350      1
0.975      1
1.126      1
0.000      1
Name: Rating, dtype: int64

# Change variables type
twitter_complete.tweet_id = twitter_complete.tweet_id.astype(str)
img_pred_clean.tweet_id = img_pred_clean.tweet_id.astype(str)
twitter_complete.dog_stage = twitter_complete.dog_stage.astype("category")

Question 1: Are there any variables correlated with each other?

# Plot a heatmap for the twitter_complete dataframe
sns.heatmap(twitter_complete.corr(), fmt="d")
plt.title('Correlation-matrix', size=20)
plt.savefig('heatmap');

# Visualizing the linear relationship between retweets and favorites
sns.jointplot(x="retweets", y="favorites", data=twitter_complete,kind="reg", color="purple")
plt.title('Favorite vs. Retweet Count')
plt.xlabel('Number of Retweets')
plt.ylabel('Number of Favorites');

As expected, there is no strong correlation between two different variables except for retweets and favorites in the twitter_complete dataframe.

# Plot a heatmap for the img_pred_clean dataframe
sns.heatmap(img_pred_clean.corr(), fmt="d")
plt.title('Correlation-matrix', size=20)
plt.savefig('heatmap');

# Visualizing the linear relationship between retweets and favorites
sns.jointplot(x="prediction_number", y="conf", data=img_pred_clean,kind="reg", color="purple")
plt.title('Favorite vs. Retweet Count')
plt.xlabel('Number of Retweets')
plt.ylabel('Number of Favorites');

As expected, there is no strong correlation between two different variables except for Prediction_number and Confidence, as the negative correlation was already a norm in the dataset construction.

Question 2: Distribution of favorites, retweets and rating over time?

# Convert 'timestamp' column to datetime type
twitter_complete['timestamp'] = pd.to_datetime(twitter_complete['timestamp'])
# Create a new dataframe with the column 'timestamp' as the index
df_time=twitter_complete.set_index('timestamp')

# Show columns of the new dataframe
df_time.columns

Index(['tweet_id', 'text', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'corrected_names', 'dog_stage', 'retweets',
       'favorites', 'Rating'],
      dtype='object')

# Plot the distribution of Retweets and Favorites over time
plt.style.use("seaborn")
df_time[['favorites','retweets']].plot(style='*', color=['purple','pink'], figsize=(12.2,5),fontsize=13)
plt.title('Favorites and Retweets with Time \n', fontsize=20)
plt.xlabel('Date', fontsize=17)
plt.ylabel('Count', fontsize=17);

As can be seen in the plot, the number of favorites increases over time, with a moderate number of favorites in November 2015 and growing progressively over the years until September 2017.

However, there is no progressive growth in the number of retweets, which seems to decrease its density in the second semester of the year 2017.

# Plot the distribution of Rating values over time
plt.style.use("seaborn")
df_time['Rating'].plot(style='.', color='c', figsize=(12.2,5), fontsize=13)
plt.title('Rating values over Time \n', fontsize=20)
plt.xlabel('Date' ,fontsize=17)
plt.ylabel('Count' ,fontsize=17);

In the figure above it is clearly seen how there are not ratings with values less than or equal to 0.8 (except for very specific outliers) after September 2016.

The high ratings have maintained its great popularity over time 

Question 3: What is the distribution of the number of tweets over time?

# Change timestamp type to datetime
twitter_complete.timestamp = pd.to_datetime(twitter_complete['timestamp'], format='%Y-%m-%d %H:%M:%S.%f')


# Select the data containing the information for the number of tweets over time (year, month)
timed_data = twitter_complete.groupby(pd.Grouper(key = 'timestamp', freq = "M")).count().reset_index()
timed_data = timed_data[['timestamp', 'tweet_id']]

# Set the seaborn plotting style for the plot below
plt.style.use("seaborn")
#  Plot Tweets Over Time
plt.plot(timed_data.timestamp, timed_data.tweet_id, color='purple', 
         marker = 'o', linestyle=':', linewidth=2)
plt.title('Tweets Over Time \n', fontsize=20)
plt.xlabel("Time (Year, Month)", labelpad=10, fontsize=15)
plt.ylabel("Number of tweets", labelpad=20, fontsize=15)

Text(0,0.5,'Number of tweets')

The number of tweets decreases over time, showing a sharp drop in December 2016.

Question 4: Which is the most popular dog breed?

# Choose a seaborn style
plt.style.use("seaborn-pastel")
# Plot the Most Rated Breeds with at least more than 80 records per breed
data=img_pred_clean.groupby('pred').filter(lambda x: len(x) >= 80)
data['pred'].value_counts().sort_values().plot(kind = 'barh', fontsize=14, figsize=(12.2,6), hatch='////')
plt.title('Most Popular Breeds \n', fontsize=20)
plt.xlabel("Number of tweets", labelpad=10, fontsize=15)
plt.ylabel("Dog Breed", labelpad=20, fontsize=15);

The most popular dog breeds are Golden Retriever and Labrador Retriever, followed by Chihuahua and Pembroke.

Question 5: What is the most popular dog name?

plt.style.use("seaborn-pastel")
df_names=twitter_complete.corrected_names.value_counts()
df_names[df_names > 7].sort_values().plot(kind='barh', legend=False, hatch='////')
plt.title('Most Popular Names \n', fontsize=20)
plt.xlabel("Number of tweets", labelpad=10, fontsize=15)
plt.ylabel("Dog Name", labelpad=20, fontsize=15);

The most popular dog names are Charlie and Lucy, followed by Cooper and Oliver.

Question 6: What is the most popular dog stage?

plt.style.use("seaborn-pastel")
df_stages=twitter_complete.dog_stage.value_counts()
df_stages.sort_values().plot(kind='barh', legend=False, hatch='////')
plt.title('Most Popular Stages \n', fontsize=20)
plt.xlabel("Number of tweets", labelpad=10, fontsize=15)
plt.ylabel("Dog Stage", labelpad=20, fontsize=15)

Text(0,0.5,'Dog Stage')

The most popular dog stage is by far pupper, followed by Doggo, Puppo and Floofer.

Question 7: Which dog stage has the highest rating?

plt.style.use("seaborn-pastel")
sns.boxplot(x='dog_stage', y='Rating', data=twitter_complete)
plt.title('Dog Stages Rating \n', fontsize=20)
plt.xlabel('Dog Stage', labelpad=20, fontsize=15)
plt.ylabel('Rating', labelpad=20, fontsize=15)
plt.ylim(0.7,);

"Puppo" dogs got the highest rating and are the less rated, while "Puppers" got the lowest rating and are the most rated ones.

Question 8: Do tweets with greater rating get more retweets and favorites?

plt.scatter(twitter_complete.Rating, twitter_complete.favorites, alpha=0.8, c='purple', s=30, label="favorites", marker = '*')
plt.scatter(twitter_complete.Rating, twitter_complete.retweets, alpha=0.8, c='pink', s=30, label="retweets", marker = '*')
plt.legend(loc=2)
plt.title('Favorites and Retweets vs. Rating  \n', fontsize=20)
plt.xlabel('Rating', labelpad=20, fontsize=15)
plt.ylabel('Number of tweets', labelpad=20, fontsize=15)

Text(0,0.5,'Number of tweets')

The number of retweets and favorites increases with the Rating value, reaching the bullish peak for a rating of 13.

Question 9: Do tweets get more retweets or favorites if they have a picture?

# Create a Dataframe with only true first predictions
img_pred_1 = img_pred_clean[(img_pred_clean['prediction_number']==1)&(img_pred_clean['dog'])]
# Merge twitter_complete with img_pred_1
twitter_complete_pred1 = pd.merge(twitter_complete,img_pred_1, how = 'left',on = 'tweet_id')
twitter_complete_pred1 = twitter_complete_pred1.replace({np.nan: 'No Picture' })
twitter_complete_pred1 = twitter_complete_pred1.replace({True: 'Picture'}).drop(['prediction_number','img_num'],axis=1)

twitter_complete_pred1.head(3)

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage	retweets	favorites	Rating	jpg_url	pred	conf	dog
0	892420643555336193	2017-08-01 16:23:56	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13	10.0	Phineas	No Picture	8853	39467	1.3	No Picture	No Picture	No Picture	No Picture
1	892177421306343426	2017-08-01 00:17:27	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13	10.0	Tilly	No Picture	6514	33819	1.3	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	Chihuahua	0.323581	Picture
2	891815181378084864	2017-07-31 00:18:03	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12	10.0	Archie	No Picture	4328	25461	1.2	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	Chihuahua	0.716012	Picture

avg_conf=twitter_complete_pred1[twitter_complete_pred1['conf']!="No Picture"].conf.mean()
print('The average confidence level for the true picture`s prediction is:',avg_conf*100, '%')

The average confidence level for the true picture`s prediction is: 61.3978574033 %

ret_nopic=twitter_complete_pred1[twitter_complete_pred1['dog']=="No Picture"].retweets.sum()
ret_pic=twitter_complete_pred1[twitter_complete_pred1['dog']=="Picture"].retweets.sum()
print('The number of retweets of tweets WITH a picture is:',ret_pic)
print('The number of retweets of tweets WITHOUT a picture is:',ret_nopic)
print('The percentage difference is:',((ret_pic/(ret_nopic+ret_pic))-(ret_nopic/(ret_nopic+ret_pic)))*100,'%')

The number of retweets of tweets WITH a picture is: 4057446
The number of retweets of tweets WITHOUT a picture is: 1845634
The percentage difference is: 37.4687790103 %

fav_nopic=twitter_complete_pred1[twitter_complete_pred1['dog']=="No Picture"].favorites.sum()
fav_pic=twitter_complete_pred1[twitter_complete_pred1['dog']=="Picture"].favorites.sum()
print('The number of favorites of tweets WITH a picture is:',fav_pic)
print('The number of favorites of tweets WITHOUT a picture is:',fav_nopic)
print('The percentage difference is:',((fav_pic/(fav_nopic+fav_pic))-(fav_nopic/(fav_nopic+fav_pic)))*100,'%')

The number of favorites of tweets WITH a picture is: 13530488
The number of favorites of tweets WITHOUT a picture is: 5150552
The percentage difference is: 44.857973646 %

Therefore, tweets have more retweets and favorites if they include an image, also, this fact affects the favorites more.

Question 10: Images of the Golden Retrievers (my favorite dog breed) best rated and with the most number of favorites?

golden_retriever_df = twitter_complete.merge(img_pred_clean.loc[img_pred_clean['pred'] == 'golden_retriever',
                                                     ['tweet_id','jpg_url','pred']],how = 'right',on = 'tweet_id')
top_golden_retriever=golden_retriever_df[(golden_retriever_df['Rating']==1.20)&(golden_retriever_df['corrected_names']!=np.nan)]
top_favs_golden_retriever=top_golden_retriever.sort_values(by = 'favorites', ascending=False)
top_favs_golden_retriever.head()

	tweet_id	timestamp	text	expanded_urls	rating_numerator	rating_denominator	corrected_names	dog_stage	retweets	favorites	Rating	jpg_url	pred
30	837820167694528512	2017-03-04 00:21:08	Here's a pupper before and after being asked "...	https://twitter.com/dog_rates/status/837820167...	12.0	10.0	NaN	pupper	8952.0	37277.0	1.2	https://pbs.twimg.com/media/C6CI_jbVAAA3-a1.jpg	golden_retriever
56	814153002265309185	2016-12-28 16:56:16	This is Oshie. He's ready to party. Bought tha...	https://twitter.com/dog_rates/status/814153002...	12.0	10.0	Oshie	NaN	10080.0	32000.0	1.2	https://pbs.twimg.com/media/C0xz04SVIAAeyDb.jpg	golden_retriever
87	788765914992902144	2016-10-19 15:37:03	This is Butter. She can have whatever she want...	https://twitter.com/dog_rates/status/788765914...	12.0	10.0	Butter	NaN	12014.0	30658.0	1.2	https://pbs.twimg.com/media/CvJCabcWgAIoUxW.jpg	golden_retriever
90	786963064373534720	2016-10-14 16:13:10	This is Rory. He's got an interview in a few m...	https://twitter.com/dog_rates/status/786963064...	12.0	10.0	Rory	NaN	9327.0	29725.0	1.2	https://pbs.twimg.com/media/Cuvau3MW8AAxaRv.jpg	golden_retriever
1	888917238123831296	2017-07-23 00:22:39	This is Jim. He found a fren. Taught him how t...	https://twitter.com/dog_rates/status/888917238...	12.0	10.0	Jim	NaN	4709.0	29611.0	1.2	https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg	golden_retriever

WINNER #1

# Import necessary libraries
from PIL import Image
import requests
from io import BytesIO
# Display Images with request.get(url)
url_1=top_favs_golden_retriever.loc[30,'jpg_url']
response = requests.get(url_1)
Image.open(BytesIO(response.content))

WINNER #2

url_2=top_favs_golden_retriever.loc[56,'jpg_url']
response = requests.get(url_2)
Image.open(BytesIO(response.content))

WINNER #3

url_3=top_favs_golden_retriever.loc[87,'jpg_url']
response = requests.get(url_3)
Image.open(BytesIO(response.content))

WINNER #4

url_4=top_favs_golden_retriever.loc[90,'jpg_url']
response = requests.get(url_4)
Image.open(BytesIO(response.content))

WINNER #5

url_5=top_favs_golden_retriever.loc[1,'jpg_url']
response = requests.get(url_5)
Image.open(BytesIO(response.content))

SOURCES

• https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reset_index.html
• https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
• https://www.w3schools.com/python/python_lambda.asp
• https://www.geeksforgeeks.org/filter-in-python/
• https://www.dataquest.io/blog/python-datetime-tutorial/
• https://realpython.com/python-matplotlib-guide/
• https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/categorical_variables.html#sphx-glr-gallery-lines-bars-and-markers-categorical-variables-py
• https://medium.com/towards-artificial-intelligence/matplotlib-complete-beginners-guide-to-line-plots-a436e18d69e4
• https://seaborn.pydata.org/generated/seaborn.jointplot.html#seaborn.jointplot
• https://stackoverflow.com/questions/7391945/how-do-i-read-image-data-from-a-url-in-python